A Survey of Moving Target Defenses for Network Security
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paper_title: Network function virtualization: Challenges and opportunities for innovations

paper_content:

Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.
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paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
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paper_title: Computing Stackelberg equilibria in discounted stochastic games

paper_content:

Stackelberg games increasingly influence security policies deployed in real-world settings. Much of the work to date focuses on devising a fixed randomized strategy for the defender, accounting for an attacker who optimally responds to it. In practice, defense policies are often subject to constraints and vary over time, allowing an attacker to infer characteristics of future policies based on current observations. A defender must therefore account for an attacker's observation capabilities in devising a security policy. We show that this general modeling framework can be captured using stochastic Stackelberg games (SSGs), where a defender commits to a dynamic policy to which the attacker devises an optimal dynamic response. We then offer the following contributions. 1) We show that Markov stationary policies suffice in SSGs, 2) present a finite-time mixedinteger non-linear program for computing a Stackelberg equilibrium in SSGs, and 3) present a mixed-integer linear program to approximate it. 4)We illustrate our algorithms on a simple SSG representing an adversarial patrolling scenario, where we study the impact of attacker patience and risk aversion on optimal defense policies.
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paper_title: SeReNe: On Establishing Secure and Resilient Networking Services for an SDN-based Multi-tenant Datacenter Environment

paper_content:

In the current enterprise data enter networking environment, a major hurdle in the development of network security is the lack of an orchestrated and resilient defensive mechanism that uses well-established quantifiable metrics, models, and evaluation methods. In this position paper, we describe an emerging Secure and Resilient Networking (SeReNe) service model to establish a programmable and dynamic defensive mechanism that can adjust the system's networking resources such as topology, bandwidth allocation, and traffic/flow forwarding policies, according to the network security situations. We posit that this requires addressing two interdependent technical areas: (a) a Moving Target Defense (MTD) framework both at networking and software levels, and (b) an Adaptive Security-enabled Traffic Engineering (ASeTE) approach to select optimal countermeasures by considering the effectiveness of countermeasures and network bandwidth allocations while minimizing the intrusiveness to the applications and the cost of deploying the countermeasures. We believe that our position can greatly benefit the virtual networking system established in data Centerior enterprise virtual networking systems that have adopted latest Open Flow technologies.
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paper_title: Network function virtualization: Challenges and opportunities for innovations

paper_content:

Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.
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paper_title: A Study on Advanced Persistent Threats

paper_content:

A recent class of threats, known as Advanced Persistent Threats (APTs), has drawn increasing attention from researchers, primarily from the industrial security sector. APTs are cyber attacks executed by sophisticated and well-resourced adversaries targeting specific information in high-profile companies and governments, usually in a long term campaign involving different steps. To a significant extent, the academic community has neglected the specificity of these threats and as such an objective approach to the APT issue is lacking. In this paper, we present the results of a comprehensive study on APT, characterizing its distinguishing characteristics and attack model, and analyzing techniques commonly seen in APT attacks. We also enumerate some non-conventional countermeasures that can help to mitigate APTs, hereby highlighting the directions for future research.
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paper_title: Survey of Cyber Moving Target Techniques

paper_content:

Abstract : This survey provides an overview of different cyber moving target techniques, their threat models, and their technical details. A cyber moving target technique refers to any technique that attempts to defend a system and increase the complexity of cyber attacks by making the system less homogeneous, less static, and less deterministic. In this survey, we describe the technical details of each technique, identify the proper threat model associated with the technique, and identify its implementation and operational cost. Moreover, we describe the weaknesses of each technique based on the current proposed attacks and bypassing exploits, and provide possible directions for future research in that area.
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paper_title: A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

paper_content:

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.
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paper_title: Moving target defense: state of the art and characteristics

paper_content:

Moving target defense (MTD) has emerged as one of the game-changing themes to alter the asymmetric situation between attacks and defenses in cyber-security. Numerous related works involving several facets of MTD have been published. However, comprehensive analyses and research on MTD are still absent. In this paper, we present a survey on MTD technologies to scientifically and systematically introduce, categorize, and summarize the existing research works in this field. First, a new security model is introduced to describe the changes in the traditional defense paradigm and security model caused by the introduction of MTD. A function-and-movement model is provided to give a panoramic overview on different perspectives for understanding the existing MTD research works. Then a systematic interpretation of published literature is presented to describe the state of the art of the three main areas in the MTD field, namely, MTD theory, MTD strategy, and MTD evaluation. Specifically, in the area of MTD strategy, the common characteristics shared by the MTD strategies to improve system security and effectiveness are identified and extrapolated. Thereafter, the methods to implement these characteristics are concluded. Moreover, the MTD strategies are classified into three types according to their specific goals, and the necessary and sufficient conditions of each type to create effective MTD strategies are then summarized, which are typically one or more of the aforementioned characteristics. Finally, we provide a number of observations for the future direction in this field, which can be helpful for subsequent researchers.
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paper_title: Quantification of moving target cyber defenses

paper_content:

Current network and information systems are static, making it simple for attackers to maintain an advantage. Adaptive defenses, such as Moving Target Defenses (MTD) have been developed as potential “game-changers” in an effort to increase the attacker’s workload. With many new methods being developed, it is difficult to accurately quantify and compare their overall costs and effectiveness. This paper compares the tradeoffs between current approaches to the quantification of MTDs. We present results from an expert opinion survey on quantifying the overall effectiveness, upfront and operating costs of a select set of MTD techniques. We find that gathering informed scientific opinions can be advantageous for evaluating such new technologies as it offers a more comprehensive assessment. We end by presenting a coarse ordering of a set of MTD techniques from most to least dominant. We found that seven out of 23 methods rank as the more dominant techniques. Five of which are techniques of either address space layout randomization or instruction set randomization. The remaining two techniques are applicable to software and computer platforms. Among the techniques that performed the worst are those primarily aimed at network randomization.
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paper_title: A Study on Advanced Persistent Threats

paper_content:

A recent class of threats, known as Advanced Persistent Threats (APTs), has drawn increasing attention from researchers, primarily from the industrial security sector. APTs are cyber attacks executed by sophisticated and well-resourced adversaries targeting specific information in high-profile companies and governments, usually in a long term campaign involving different steps. To a significant extent, the academic community has neglected the specificity of these threats and as such an objective approach to the APT issue is lacking. In this paper, we present the results of a comprehensive study on APT, characterizing its distinguishing characteristics and attack model, and analyzing techniques commonly seen in APT attacks. We also enumerate some non-conventional countermeasures that can help to mitigate APTs, hereby highlighting the directions for future research.
---
paper_title: A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

paper_content:

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.
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paper_title: The design and implementation of tripwire: a file system integrity checker

paper_content:

At the heart of most computer systems is a file system. The file system contains user data, executable programs, configuration and authorization information, and (usually) the base executable version of the operating system itself. The ability to monitor file systems for unauthorized or unexpected changes gives system administrators valuable data for protecting and maintaining their systems. However, in environments of many networked heterogeneous platforms with different policies and software, the task of monitoring changes becomes quite daunting. Tripwire is tool that aids UNIX system administrators and users in monitoring a designated set of files and directories for any changes. Used with system files on a regular (e.g., daily) basis, Tripwire can notify system administrators of corrupted or altered files, so corrective actions may be taken in a timely manner. Tripwire may also be used on user or group files or databases to signal changes. This paper describes the design and implementation of the Tripwire tool. It uses interchangeable “signature” (usually, message digest) routines to identify changes in files, and is highly configurable. Tripwire is no-cost software, available on the Internet, and is currently in use on thousands of machines around the world.
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paper_title: HIDS: A host based intrusion detection system for cloud computing environment

paper_content:

The paper reports a host based intrusion detection model for Cloud computing environment along with its implementation and analysis. This model alerts the Cloud user against the malicious activities within the system by analyzing the system call traces. The method analyses only selective system call traces, the failed system call trace, rather than all. An early detection of intrusions with reduced computational burden can be possible with this feature. The reported model provides security as a service (SecaaS) in the infrastructure layer of the Cloud environment. Implementation result shows 96 % average intrusion detection sensitivity.
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paper_title: Intrusion prevention system design

paper_content:

It is one of the good solutions for improving network security to integrate many kinds of security techniques. Firewall and intrusion detection system can enforce security of the network effectively, but there are also drawbacks existing in themselves. Intrusion prevention system (IPS) is a technique combining the techniques of the firewall with that of the IDS properly. The characters of the IPS are introduced firstly in this paper, and then a scenario of IPS based network is described. Finally, a distributed IPS: IPS design based on SNMP is put forward, the function and implementation of each part of IPS is explained in detail.
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paper_title: A brief study and comparison of Snort and Bro Open Source Network Intrusion Detection Systems

paper_content:

Security administration plays a vital role in network management tasks. The intrusion detection systems are primarily designed to protect the availability, confidentiality and integrity of critical network information systems. There are plenty of IDSes to choose from, both commercial and open source. Since most of the commercial intrusion detection systems are at typically thousands of dollars and they tend to represent a significant resource requirement in themselves, for small networks, use of such IDS is not feasible. Therefore mostly open source IDS are being used. This paper provides a general working behaviour, features and comparison of two most popular open source network IDS - SNORT & BRO. Keywords-alerts, intrusion, logging, network traffic, open source, packets
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paper_title: Web Analytics for Security Informatics

paper_content:

An enormous volume of security-relevant information is present on the Web, for instance in the content produced each day by millions of bloggers worldwide, but discovering and making sense of these data is very challenging. This paper considers the problem of exploring and analyzing the Web to realize three fundamental objectives: 1.) security-relevant information is covery, 2.) target situational awareness, typically by making (near) real-time inferences concerning events and activities from available observations, and 3.) predictive analysis, to include providing early warning for crises and forming predictions regarding likely outcomes of emerging issues and contemplated interventions. The proposed approach involves collecting and integrating three types of Web data -- textual, relational, and temporal -- to perform assessments and generate insights that would be difficult or impossible to obtain using standard methods. We demonstrate the efficacy of the framework by summarizing a number of successful real-world deployments of the methodology.
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paper_title: Bypassing Data Execution Prevention on MicrosoftWindows XP SP2

paper_content:

The evolution of Microsoft Windows from a desktop operating system into a server operating system has brought attention to and concern of some severe security issues. Attacks that exploited buffer overflows started appearing for the services that were used on the new server operating system. Recently, Microsoft decided to implement a protective measure: data execution prevention - DEP in two of their products: Service Pack 2 for Windows XP and Service Pack 1 for Windows 2003. The measure has been implemented as one of the core security mechanisms with the intention to prevent the attackers from breaking into the system i.e., to prevent the execution of code in non-executable memory regions. In this paper we show that the initial implementation of the software for DEP in Windows XP Service Pack 2 is actually not at all secure and that stack overflow attacks against DEP are as effective as attacks against systems that do not have DEP
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paper_title: Honeypot-based forensics

paper_content:

Some attacks on honeypots are very frequent and repetitive. In addition, such repetitive attacks generate a very large amount of data. In this paper, we show that it might be misleading to consider general statistics obtained on these data without carrying an in depth analysis of the various processes that have led to their creation. We show that such analysis can be done by means of a simple clustering approach. We present an algorithm to characterize the root causes of these attacks. This algorithm enables us to obtain precious and non trivial information to identify the various attacks targeting our environment. We use this algorithm to identify root causes of the data collected from our honeypot environment. We demonstrate that identifying the root causes is a prerequisite for a better understanding of malicious activity observed thanks to honeypots environments. Finally, we hope this work will open new avenues for the ongoing work related to honeynets.
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paper_title: Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

paper_content:

Malware is constantly adapting in order to avoid detection. Model-based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution (PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness.
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paper_title: Intriguing properties of neural networks

paper_content:

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. ::: First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. ::: Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.
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paper_title: MLDS: Multi-Layer Defense System for Preventing Advanced Persistent Threats

paper_content:

Here we report on the issue of Advanced Persistent Threats (APT), which use malware for the purpose of leaking the data of large corporations and government agencies. APT attacks target systems continuously by utilizing intelligent and complex technologies. To overthrow the elaborate security network of target systems, it conducts an attack after undergoing a pre-reconnaissance phase. An APT attack causes financial loss, information leakage, etc. They can easily bypass the antivirus system of a target system. In this paper, we propose a Multi-Layer Defense System (MLDS) that can defend against APT. This system applies a reinforced defense system by collecting and analyzing log information and various information from devices, by installing the agent on the network appliance, server and end-user. It also discusses how to detect an APT attack when one cannot block the initial intrusion while continuing to conduct other activities. Thus, this system is able to minimize the possibility of initial intrusion and damages of the system by promptly responding through rapid detection of an attack when the target system is attacked.
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paper_title: A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

paper_content:

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.
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paper_title: Glossary of key information security terms

paper_content:

The National Institute of Standards and Technology (NIST) has received numerous requests to provide a summary glossary for our publications and other relevant sources, and to make the glossary available to practitioners. As a result of these requests, this glossary of common security terms has been extracted from NIST Federal Information Processing Standards (FIPS), the Special Publication (SP) 800 series, NIST Interagency Reports (NISTIRs), and from the Committee for National Security Systems Instruction 4009 (CNSSI-4009). This glossary includes most of the terms in the NIST publications. It also contains nearly all of the terms and definitions from CNSSI-4009. This glossary provides a central resource of terms and definitions most commonly used in NIST information security publications and in CNSS information assurance publications. For a given term, we do not include all definitions in NIST documents – especially not from the older NIST publications. Since draft documents are not stable, we do not refer to terms/definitions in them. Each entry in the glossary points to one or more source NIST publications, and/or CNSSI-4009, and/or supplemental sources where appropriate. The NIST publications referenced are the most recent versions of those publications (as of the date of this document).
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paper_title: Ensuring Deception Consistency for FTP Services Hardened against Advanced Persistent Threats

paper_content:

As evidenced by numerous high-profile security incidents such as the Target data breach and the Equifax hack, APTs (Advanced Persistent Threats) can significantly compromise the trustworthiness of cyber space. This work explores how to improve the effectiveness of cyber deception in hardening FTP (File Transfer Protocol) services against APTs. The main objective of our work is to ensure deception consistency: when the attackers are trapped, they can only make observations that are consistent with what they have seen already so that they cannot recognize the deceptive environment. To achieve deception consistency, we use logic constraints to characterize an attacker's best knowledge (either positive, negative, or uncertain). When migrating the attacker's FTP connection into a contained environment, we use these logic constraints to instantiate a new FTP file system that is guaranteed free of inconsistency. We performed deception experiments with student participants who just completed a computer security course. Following the design of Turing tests, we find that the participants' chances of recognizing deceptive environments are close to random guesses. Our experiments also confirm the importance of observation consistency in identifying deception.
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paper_title: A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

paper_content:

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.
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paper_title: Scalable, graph-based network vulnerability analysis

paper_content:

Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graph-based algorithms to generate attack trees (or graphs). Either structure represents all possible sequences of exploits, where any given exploit can take advantage of the penetration achieved by prior exploits in its chain, and the final exploit in the chain achieves the attacker's goal. The most recent approach in this line of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems, and there is good reason to doubt whether a model checker can handle directly a realistic set of exploits for even a modest-sized network. In this paper, we revisit the idea of attack graphs themselves, and argue that they represent more information explicitly than is necessary for the analyst. Instead, we propose a more compact and scalable representation. Although we show that it is possible to produce attack trees from our representation, we argue that more useful information can be produced, for larger networks, while bypassing the attack tree step. Our approach relies on an explicit assumption of monotonicity, which, in essence, states that the precondition of a given exploit is never invalidated by the successful application of another exploit. In other words, the attacker never needs to backtrack. The assumption reduces the complexity of the analysis problem from exponential to polynomial, thereby bringing even very large networks within reach of analysis
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paper_title: Foundations of Attack Trees

paper_content:

Attack trees have found their way to practice because they have proved to be an intuitive aid in threat analysis. Despite, or perhaps thanks to, their apparent simplicity, they have not yet been provided with an unambiguous semantics. We argue that such a formal interpretation is indispensable to precisely understand how attack trees can be manipulated during construction and analysis. We provide a denotational semantics, based on a mapping to attack suites, which abstracts from the internal structure of an attack tree, we study transformations between attack trees, and we study the attribution and projection of an attack tree.
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paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
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paper_title: Scalable Security Model Generation and Analysis Using k-importance Measures

paper_content:

Attack representation models (ARMs) (such as attack graphs, attack trees) can be used to model and assess security of a networked system. To do this, one must generate an ARM. However, generation and evaluation of the ARM suffer from a scalability problem when the size of the networked system is very large (e.g., 10,000 computer hosts in the network with a complex network topology). The main reason is that computing all possible attack scenarios to cover all aspects of an attack results in a state space explosion. One idea is to use only important hosts and vulnerabilities in the networked system to generate and evaluate security. We propose to use k-importance measures to generate a two-layer hierarchical ARM that will improve the scalability of model generation and security evaluation computational complexities. We use k 1 number of important hosts based on network centrality measures and k 2 number of significant vulnerabilities of hosts using host security metrics. We show that an equivalent security analysis can be achieved using our approach (using k-importance measures), compared to an exhaustive search.
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paper_title: Performance Analysis of Scalable Attack Representation Models

paper_content:

Attack graphs (AGs) have been widely used for security analysis. The construction of the graph-based attack models including the AG have been studied, but the security evaluation considering the full attack paths cannot be computed using existing attack models due to the scalability problem. To solve this, we propose to use hierarchical attack representation models (HARMs). First, we formulate key questions that need to be answered to compare the scalability of existing attack models. We show the scalability of the HARMs via simulations, by taking into account practical attack scenario based on various network topologies.
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paper_title: Quantifying security risk level from CVSS estimates of frequency and impact

paper_content:

Modern society relies on and profits from well-balanced computerized systems. Each of these systems has a core mission such as the correct and safe operation of safety critical systems or innovative and effective operation of e-commerce systems. It might be said that the success of these systems depends on their mission. Although the concept of ''well-balanced'' has a slightly different meaning for each of these two categories of systems, both have to meet customer needs, deliver capabilities and functions according to expectations and generate revenue to sustain today's highly competitive market. Tighter financial constraints are forcing safety critical systems away from dedicated and expensive communication regimes, such as the ownership and operation of dedicated communication links, towards reliance on third parties and standardized means of communication. As a consequence, knowledge about their internal structures and operations is more widely and publicly available and this can make them more prone to security attacks. These systems are, therefore, moving towards a remotely exploitable environment and the risks associated with this must be controlled. Risk management is a good tool for controlling risk but it has the inherent challenge of quantitatively estimating frequency and impact in an accurate and trustworthy way. Quantifying the frequency and impact of potential security threats requires experience-based data which is limited and rarely reusable because it involves company confidential data. Therefore, there is a need for publicly available data sources that can be used in risk estimation. This paper presents a risk estimation model that makes use of one such data source, the Common Vulnerability Scoring System (CVSS). The CVSS Risk Level Estimation Model estimates a security risk level from vulnerability information as a combination of frequency and impact estimates derived from the CVSS. It is implemented as a Bayesian Belief Network (BBN) topology, which allows not only the use of CVSS-based estimates but also the combination of disparate information sources and, thus, provides the ability to use whatever risk information that is available. The model is demonstrated using a safety- and mission-critical system for drilling operational support, the Measurement and Logging While Drilling (M/LWD) system.
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paper_title: Tools for Generating and Analyzing Attack Graphs

paper_content:

Attack graphs depict ways in which an adversary exploits system vulnerabilities to achieve a desired state. System administrators use attack graphs to determine how vulnerable their systems are and to determine what security measures to deploy to defend their systems. In this paper, we present details of an example to illustrate how we specify and analyze network attack models. We take these models as input to our attack graph tools to generate attack graphs automatically and to analyze system vulnerabilities. While we have published our generation and analysis algorithms in earlier work, the presentation of our example and toolkit is novel to this paper.
---
paper_title: Towards a Theory of Moving Target Defense

paper_content:

The static nature of cyber systems gives attackers the advantage of time. Fortunately, a new approach, called the Moving Target Defense (MTD) has emerged as a potential solution to this problem. While promising, there is currently little research to show that MTD systems can work effectively in real systems. In fact, there is no standard definition of what an MTD is, what is meant by attack surface, or metrics to define the effectiveness of such systems. In this paper, we propose an initial theory that will begin to answer some of those questions. The paper defines the key concepts required to formally talk about MTD systems and their basic properties. It also discusses three essential problems of MTD systems, which include the MTD Problem (or how to select the next system configuration), the Adaptation Selection Problem, and the Timing Problem. We then formalize the MTD Entropy Hypothesis, which states that the greater the entropy of the system's configuration, the more effective the MTD system.
---
paper_title: Value-function reinforcement learning in Markov games

paper_content:

Markov games are a model of multiagent environments that are convenient for studying multiagent reinforcement learning. This paper describes a set of reinforcement-learning algorithms based on estimating value functions and presents convergence theorems for these algorithms. The main contribution of this paper is that it presents the convergence theorems in a way that makes it easy to reason about the behavior of simultaneous learners in a shared environment.
---
paper_title: Random Host Mutation for Moving Target Defense

paper_content:

Exploiting static configuration of networks and hosts has always been a great advantage for design and launching of decisive attacks. Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. At the same time, knowing IP addresses is required for service reachability in IP networks, which makes complete concealment of IP address for servers infeasible. In addition, changing IP addresses too frequently may cause serious ramifications including service interruptions, routing inflation, delays and security violations. In this paper, we present a novel approach that turns end-hosts into untraceable moving targets by transparently mutating their IP addresses in an intelligent and unpredictable fashion and without sacrificing network integrity, manageability or performance. The presented technique is called Random Host Mutation (RHM). In RHM, moving target hosts are assigned virtual IP addresses that change randomly and synchronously in a distributed fashion over time. In order to prevent disruption of active connections, the IP address mutation is managed by network appliances and totally transparent to end-host. RHM employs multi-level optimized mutation techniques that maximize uncertainty in adversary scanning by effectively using the whole available address range, while at the same time minimizing the size of routing tables, and reconfiguration updates. RHM can be transparently deployed on existing networks on end-hosts or network elements. Our analysis, implementation and evaluation show that RHM can effectively defend against stealthy scanning, many types of worm propagation and attacks that require reconnaissance for successful launching. We also show the performance bounds for moving target defense in a practical network setup.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: Deceiving Cyber Adversaries: A Game Theoretic Approach

paper_content:

An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender's exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches.
---
paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: Towards a Theory of Moving Target Defense

paper_content:

The static nature of cyber systems gives attackers the advantage of time. Fortunately, a new approach, called the Moving Target Defense (MTD) has emerged as a potential solution to this problem. While promising, there is currently little research to show that MTD systems can work effectively in real systems. In fact, there is no standard definition of what an MTD is, what is meant by attack surface, or metrics to define the effectiveness of such systems. In this paper, we propose an initial theory that will begin to answer some of those questions. The paper defines the key concepts required to formally talk about MTD systems and their basic properties. It also discusses three essential problems of MTD systems, which include the MTD Problem (or how to select the next system configuration), the Adaptation Selection Problem, and the Timing Problem. We then formalize the MTD Entropy Hypothesis, which states that the greater the entropy of the system's configuration, the more effective the MTD system.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: Software diversity: Security, Entropy and Game Theory

paper_content:

Although many have recognized the risks of software monocultures, it is not currently clear how much and what kind of diversity would be needed to address these risks. Here we attempt to provide insight into this issue using a simple model of hosts and vulnerabilities connected in a bipartite graph. We use this graph to compute diversity metrics as Renyi entropy and to formulate an anti-coordination game to understand why computer host owners would choose to diversify. Since security isn't the only factor considered when choosing software in the real world, we propose a slight variation of the popular security wargame Capture the Flag that can serve as a testbed for understanding the utility of diversity as a defense strategy.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: Empirical Game-Theoretic Analysis for Moving Target Defense

paper_content:

The effectiveness of a moving target defense depends on how it is deployed through specific system operations over time, and how attackers may respond to this deployment. We define a generic cyber-defense scenario, and examine the interplay between attack and defense strategies using empirical game-theoretic techniques. In this approach, the scenario is defined procedurally by a simulator, and data derived from systematic simulation is used to induce a game model. We explore a space of 72 game instances, defined by differences in agent objectives, attack cost, and ability of the defender to detect attack actions. We observe a range of qualitative strategic behaviors, which vary in clear patterns across environmental conditions. In particular, we find that the efficacy of deterrent defense is critically sensitive to detection capability, and in the absence of perfect detection the defender is often driven to proactive moving-target actions.
---
paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: Random Host Mutation for Moving Target Defense

paper_content:

Exploiting static configuration of networks and hosts has always been a great advantage for design and launching of decisive attacks. Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. At the same time, knowing IP addresses is required for service reachability in IP networks, which makes complete concealment of IP address for servers infeasible. In addition, changing IP addresses too frequently may cause serious ramifications including service interruptions, routing inflation, delays and security violations. In this paper, we present a novel approach that turns end-hosts into untraceable moving targets by transparently mutating their IP addresses in an intelligent and unpredictable fashion and without sacrificing network integrity, manageability or performance. The presented technique is called Random Host Mutation (RHM). In RHM, moving target hosts are assigned virtual IP addresses that change randomly and synchronously in a distributed fashion over time. In order to prevent disruption of active connections, the IP address mutation is managed by network appliances and totally transparent to end-host. RHM employs multi-level optimized mutation techniques that maximize uncertainty in adversary scanning by effectively using the whole available address range, while at the same time minimizing the size of routing tables, and reconfiguration updates. RHM can be transparently deployed on existing networks on end-hosts or network elements. Our analysis, implementation and evaluation show that RHM can effectively defend against stealthy scanning, many types of worm propagation and attacks that require reconnaissance for successful launching. We also show the performance bounds for moving target defense in a practical network setup.
---
paper_title: Towards a Theory of Moving Target Defense

paper_content:

The static nature of cyber systems gives attackers the advantage of time. Fortunately, a new approach, called the Moving Target Defense (MTD) has emerged as a potential solution to this problem. While promising, there is currently little research to show that MTD systems can work effectively in real systems. In fact, there is no standard definition of what an MTD is, what is meant by attack surface, or metrics to define the effectiveness of such systems. In this paper, we propose an initial theory that will begin to answer some of those questions. The paper defines the key concepts required to formally talk about MTD systems and their basic properties. It also discusses three essential problems of MTD systems, which include the MTD Problem (or how to select the next system configuration), the Adaptation Selection Problem, and the Timing Problem. We then formalize the MTD Entropy Hypothesis, which states that the greater the entropy of the system's configuration, the more effective the MTD system.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

paper_content:

Present attack methods can make state-of-the-art classification systems based on deep neural networks mis-classify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for ‘boosting’ the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image at test time, a constituent network is randomly selected based on a mixed policy. To obtain this policy, we formulate the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that our approach MTDeep, reduces misclassification on perturbed images for various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms alone. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine

paper_content:

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender’s strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.
---
paper_title: Software diversity: Security, Entropy and Game Theory

paper_content:

Although many have recognized the risks of software monocultures, it is not currently clear how much and what kind of diversity would be needed to address these risks. Here we attempt to provide insight into this issue using a simple model of hosts and vulnerabilities connected in a bipartite graph. We use this graph to compute diversity metrics as Renyi entropy and to formulate an anti-coordination game to understand why computer host owners would choose to diversify. Since security isn't the only factor considered when choosing software in the real world, we propose a slight variation of the popular security wargame Capture the Flag that can serve as a testbed for understanding the utility of diversity as a defense strategy.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: From physical security to cybersecurity

paper_content:

Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Computational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of security resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games.” The research challenges posed by these applications include scaling up security games to real-world-sized problems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries. In cybersecurity domain, the interaction between the defender and adversary is quite complicated with high degree of incomplete information and uncertainty. While solutions have been proposed for parts of the problem space in cybersecurity, the need of the hour is a comprehensive understanding of the whole space including the interaction with the adversary. We highlight the innovations in security games that could be used to tackle the game problem in cybersecurity.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: Deceiving Cyber Adversaries: A Game Theoretic Approach

paper_content:

An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender's exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches.
---
paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: Empirical Game-Theoretic Analysis for Moving Target Defense

paper_content:

The effectiveness of a moving target defense depends on how it is deployed through specific system operations over time, and how attackers may respond to this deployment. We define a generic cyber-defense scenario, and examine the interplay between attack and defense strategies using empirical game-theoretic techniques. In this approach, the scenario is defined procedurally by a simulator, and data derived from systematic simulation is used to induce a game model. We explore a space of 72 game instances, defined by differences in agent objectives, attack cost, and ability of the defender to detect attack actions. We observe a range of qualitative strategic behaviors, which vary in clear patterns across environmental conditions. In particular, we find that the efficacy of deterrent defense is critically sensitive to detection capability, and in the absence of perfect detection the defender is often driven to proactive moving-target actions.
---
paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems

paper_content:

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine

paper_content:

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender’s strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.
---
paper_title: Software diversity: Security, Entropy and Game Theory

paper_content:

Although many have recognized the risks of software monocultures, it is not currently clear how much and what kind of diversity would be needed to address these risks. Here we attempt to provide insight into this issue using a simple model of hosts and vulnerabilities connected in a bipartite graph. We use this graph to compute diversity metrics as Renyi entropy and to formulate an anti-coordination game to understand why computer host owners would choose to diversify. Since security isn't the only factor considered when choosing software in the real world, we propose a slight variation of the popular security wargame Capture the Flag that can serve as a testbed for understanding the utility of diversity as a defense strategy.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: CHAOS: an SDN-based Moving Target Defense System

paper_content:

Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
---
paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: FlipIt: The Game of “Stealthy Takeover”

paper_content:

Recent targeted attacks have increased significantly in sophistication, undermining the fundamental assumptions on which most cryptographic primitives rely for security. For instance, attackers launching an Advanced Persistent Threat (APT) can steal full cryptographic keys, violating the very secrecy of "secret" keys that cryptographers assume in designing secure protocols. In this article, we introduce a game-theoretic framework for modeling various computer security scenarios prevalent today, including targeted attacks. We are particularly interested in situations in which an attacker periodically compromises a system or critical resource completely, learns all its secret information and is not immediately detected by the system owner or defender. We propose a two-player game between an attacker and defender called FlipIt or The Game of "Stealthy Takeover." In FlipIt, players compete to control a shared resource. Unlike most existing games, FlipIt allows players to move at any given time, taking control of the resource. The identity of the player controlling the resource, however, is not revealed until a player actually moves. To move, a player pays a certain move cost. The objective of each player is to control the resource a large fraction of time, while minimizing his total move cost. FlipIt provides a simple and elegant framework in which we can formally reason about the interaction between attackers and defenders in practical scenarios. In this article, we restrict ourselves to games in which one of the players (the defender) plays with a renewal strategy, one in which the intervals between consecutive moves are chosen independently and uniformly at random from a fixed probability distribution. We consider attacker strategies ranging in increasing sophistication from simple periodic strategies (with moves spaced at equal time intervals) to more complex adaptive strategies, in which moves are determined based on feedback received during the game. For different classes of strategies employed by the attacker, we determine strongly dominant strategies for both players (when they exist), strategies that achieve higher benefit than all other strategies in a particular class. When strongly dominant strategies do not exist, our goal is to characterize the residual game consisting of strategies that are not strongly dominated by other strategies. We also prove equivalence or strict inclusion of certain classes of strategies under different conditions. Our analysis of different FlipIt variants teaches cryptographers, system designers, and the community at large some valuable lessons: ::: ::: Interestingly, FlipIt finds applications in other security realms besides modeling of targeted attacks. Examples include cryptographic key rotation, password changing policies, refreshing virtual machines, and cloud auditing.
---
paper_title: Empirical Game-Theoretic Analysis for Moving Target Defense

paper_content:

The effectiveness of a moving target defense depends on how it is deployed through specific system operations over time, and how attackers may respond to this deployment. We define a generic cyber-defense scenario, and examine the interplay between attack and defense strategies using empirical game-theoretic techniques. In this approach, the scenario is defined procedurally by a simulator, and data derived from systematic simulation is used to induce a game model. We explore a space of 72 game instances, defined by differences in agent objectives, attack cost, and ability of the defender to detect attack actions. We observe a range of qualitative strategic behaviors, which vary in clear patterns across environmental conditions. In particular, we find that the efficacy of deterrent defense is critically sensitive to detection capability, and in the absence of perfect detection the defender is often driven to proactive moving-target actions.
---
paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: Random Host Mutation for Moving Target Defense

paper_content:

Exploiting static configuration of networks and hosts has always been a great advantage for design and launching of decisive attacks. Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. At the same time, knowing IP addresses is required for service reachability in IP networks, which makes complete concealment of IP address for servers infeasible. In addition, changing IP addresses too frequently may cause serious ramifications including service interruptions, routing inflation, delays and security violations. In this paper, we present a novel approach that turns end-hosts into untraceable moving targets by transparently mutating their IP addresses in an intelligent and unpredictable fashion and without sacrificing network integrity, manageability or performance. The presented technique is called Random Host Mutation (RHM). In RHM, moving target hosts are assigned virtual IP addresses that change randomly and synchronously in a distributed fashion over time. In order to prevent disruption of active connections, the IP address mutation is managed by network appliances and totally transparent to end-host. RHM employs multi-level optimized mutation techniques that maximize uncertainty in adversary scanning by effectively using the whole available address range, while at the same time minimizing the size of routing tables, and reconfiguration updates. RHM can be transparently deployed on existing networks on end-hosts or network elements. Our analysis, implementation and evaluation show that RHM can effectively defend against stealthy scanning, many types of worm propagation and attacks that require reconnaissance for successful launching. We also show the performance bounds for moving target defense in a practical network setup.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine

paper_content:

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender’s strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.
---
paper_title: Software diversity: Security, Entropy and Game Theory

paper_content:

Although many have recognized the risks of software monocultures, it is not currently clear how much and what kind of diversity would be needed to address these risks. Here we attempt to provide insight into this issue using a simple model of hosts and vulnerabilities connected in a bipartite graph. We use this graph to compute diversity metrics as Renyi entropy and to formulate an anti-coordination game to understand why computer host owners would choose to diversify. Since security isn't the only factor considered when choosing software in the real world, we propose a slight variation of the popular security wargame Capture the Flag that can serve as a testbed for understanding the utility of diversity as a defense strategy.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: CHAOS: an SDN-based Moving Target Defense System

paper_content:

Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
---
paper_title: Nmap Network Scanning: The Official Nmap Project Guide to Network Discovery and Security Scanning

paper_content:

Nmap Network Scanning is the official guide to the Nmap Security Scanner, a free and open source utility used by millions of people for network discovery, administration, and security auditing. From explaining port scanning basics for novices to detailing low-level packet crafting methods used by advanced hackers, this book suits all levels of security and networking professionals. A 42-page reference guide documents every Nmap feature and option, while the rest of the book demonstrates how to apply those features to quickly solve real-world tasks. Examples and diagrams show actual communication on the wire. Topics include subverting firewalls and intrusion detection systems, optimizing Nmap performance, and automating common networking tasks with the Nmap Scripting Engine. Hints and instructions are provided for common uses such as taking network inventory, penetration testing, detecting rogue wireless access points, and quashing network worm outbreaks. Nmap runs on Windows, Linux, and Mac OS X. Nmap's original author, Gordon "Fyodor" Lyon, wrote this book to share everything he has learned about network scanning during more than 11 years of Nmap development. Visit http://nmap.org/book for more information and sample chapters.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: Secure traceroute to detect faulty or malicious routing

paper_content:

Network routing is vulnerable to disruptions caused by malfunctioning or malicious routers that draw traffic towards themselves but fail to correctly forward the traffic. The existing approach to addressing this problem is to secure the routing protocol by having it validate routing updates, i.e., verify their authenticity, accuracy, and/or consistency. In this paper, we argue that it is also important to ensure the robustness of packet forwarding itself. To this end, we propose a different approach, the central idea of which is a secure traceroute protocol that enables end hosts or routers to detect and locate the source of (arbitrarily severe) routing misbehaviors, so that appropriate action can be taken.
---
paper_title: FlipIt: The Game of “Stealthy Takeover”

paper_content:

Recent targeted attacks have increased significantly in sophistication, undermining the fundamental assumptions on which most cryptographic primitives rely for security. For instance, attackers launching an Advanced Persistent Threat (APT) can steal full cryptographic keys, violating the very secrecy of "secret" keys that cryptographers assume in designing secure protocols. In this article, we introduce a game-theoretic framework for modeling various computer security scenarios prevalent today, including targeted attacks. We are particularly interested in situations in which an attacker periodically compromises a system or critical resource completely, learns all its secret information and is not immediately detected by the system owner or defender. We propose a two-player game between an attacker and defender called FlipIt or The Game of "Stealthy Takeover." In FlipIt, players compete to control a shared resource. Unlike most existing games, FlipIt allows players to move at any given time, taking control of the resource. The identity of the player controlling the resource, however, is not revealed until a player actually moves. To move, a player pays a certain move cost. The objective of each player is to control the resource a large fraction of time, while minimizing his total move cost. FlipIt provides a simple and elegant framework in which we can formally reason about the interaction between attackers and defenders in practical scenarios. In this article, we restrict ourselves to games in which one of the players (the defender) plays with a renewal strategy, one in which the intervals between consecutive moves are chosen independently and uniformly at random from a fixed probability distribution. We consider attacker strategies ranging in increasing sophistication from simple periodic strategies (with moves spaced at equal time intervals) to more complex adaptive strategies, in which moves are determined based on feedback received during the game. For different classes of strategies employed by the attacker, we determine strongly dominant strategies for both players (when they exist), strategies that achieve higher benefit than all other strategies in a particular class. When strongly dominant strategies do not exist, our goal is to characterize the residual game consisting of strategies that are not strongly dominated by other strategies. We also prove equivalence or strict inclusion of certain classes of strategies under different conditions. Our analysis of different FlipIt variants teaches cryptographers, system designers, and the community at large some valuable lessons: ::: ::: Interestingly, FlipIt finds applications in other security realms besides modeling of targeted attacks. Examples include cryptographic key rotation, password changing policies, refreshing virtual machines, and cloud auditing.
---
paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
---
paper_title: MTD Analysis and evaluation framework in Software Defined Network (MASON)

paper_content:

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.
---
paper_title: Random Host Mutation for Moving Target Defense

paper_content:

Exploiting static configuration of networks and hosts has always been a great advantage for design and launching of decisive attacks. Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. At the same time, knowing IP addresses is required for service reachability in IP networks, which makes complete concealment of IP address for servers infeasible. In addition, changing IP addresses too frequently may cause serious ramifications including service interruptions, routing inflation, delays and security violations. In this paper, we present a novel approach that turns end-hosts into untraceable moving targets by transparently mutating their IP addresses in an intelligent and unpredictable fashion and without sacrificing network integrity, manageability or performance. The presented technique is called Random Host Mutation (RHM). In RHM, moving target hosts are assigned virtual IP addresses that change randomly and synchronously in a distributed fashion over time. In order to prevent disruption of active connections, the IP address mutation is managed by network appliances and totally transparent to end-host. RHM employs multi-level optimized mutation techniques that maximize uncertainty in adversary scanning by effectively using the whole available address range, while at the same time minimizing the size of routing tables, and reconfiguration updates. RHM can be transparently deployed on existing networks on end-hosts or network elements. Our analysis, implementation and evaluation show that RHM can effectively defend against stealthy scanning, many types of worm propagation and attacks that require reconnaissance for successful launching. We also show the performance bounds for moving target defense in a practical network setup.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: MTD Analysis and evaluation framework in Software Defined Network (MASON)

paper_content:

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: Random Host Mutation for Moving Target Defense

paper_content:

Exploiting static configuration of networks and hosts has always been a great advantage for design and launching of decisive attacks. Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. At the same time, knowing IP addresses is required for service reachability in IP networks, which makes complete concealment of IP address for servers infeasible. In addition, changing IP addresses too frequently may cause serious ramifications including service interruptions, routing inflation, delays and security violations. In this paper, we present a novel approach that turns end-hosts into untraceable moving targets by transparently mutating their IP addresses in an intelligent and unpredictable fashion and without sacrificing network integrity, manageability or performance. The presented technique is called Random Host Mutation (RHM). In RHM, moving target hosts are assigned virtual IP addresses that change randomly and synchronously in a distributed fashion over time. In order to prevent disruption of active connections, the IP address mutation is managed by network appliances and totally transparent to end-host. RHM employs multi-level optimized mutation techniques that maximize uncertainty in adversary scanning by effectively using the whole available address range, while at the same time minimizing the size of routing tables, and reconfiguration updates. RHM can be transparently deployed on existing networks on end-hosts or network elements. Our analysis, implementation and evaluation show that RHM can effectively defend against stealthy scanning, many types of worm propagation and attacks that require reconnaissance for successful launching. We also show the performance bounds for moving target defense in a practical network setup.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: A Survey of Game Theory as Applied to Network Security

paper_content:

Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: Moving Target Defense for Web Applications using Bayesian Stackelberg Games

paper_content:

Vulnerabilities in web applications allow hackers to access and/or modify restricted data. Here the hackers have the opportunity to perform reconnaissance so as to gain knowledge about the web application layout before launching an attack, whereas the defender (administrator of the web application) must secure the application even with its potential vulnerabilities. In order to mask such vulnerabilities which are primarily associated with different individual configurations, Moving Target Defense systems were proposed wherein the defender switches between various configurations thereby making it difficult to attack with success, while maintaining a seamless experience for the genuine users. In this paper, we present a way to find effective switching strategies by modeling this ecosystem as a Bayesian Stackelberg game with the administrator as the leader and the hackers as the followers, which as we show succinctly captures various aspects of the Moving Target Defense systems. Furthermore, we propose ways to find the most critical vulnerabilities and the most sensitive attacker types, which are key issues in such scenarios.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
---
paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
---
paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
---
paper_title: Computing Stackelberg equilibria in discounted stochastic games

paper_content:

Stackelberg games increasingly influence security policies deployed in real-world settings. Much of the work to date focuses on devising a fixed randomized strategy for the defender, accounting for an attacker who optimally responds to it. In practice, defense policies are often subject to constraints and vary over time, allowing an attacker to infer characteristics of future policies based on current observations. A defender must therefore account for an attacker's observation capabilities in devising a security policy. We show that this general modeling framework can be captured using stochastic Stackelberg games (SSGs), where a defender commits to a dynamic policy to which the attacker devises an optimal dynamic response. We then offer the following contributions. 1) We show that Markov stationary policies suffice in SSGs, 2) present a finite-time mixedinteger non-linear program for computing a Stackelberg equilibrium in SSGs, and 3) present a mixed-integer linear program to approximate it. 4)We illustrate our algorithms on a simple SSG representing an adversarial patrolling scenario, where we study the impact of attacker patience and risk aversion on optimal defense policies.
---
paper_title: Markov Modeling of Moving Target Defense Games

paper_content:

We introduce a Markov-model-based framework for Moving Target Defense (MTD) analysis. The framework allows modeling of a broad range of MTD strategies, provides general theorems about how the probability of a successful adversary defeating an MTD strategy is related to the amount of time/cost spent by the adversary, and shows how a multilevel composition of MTD strategies can be analyzed by a straightforward combination of the analysis for each one of these strategies. Within the proposed framework we define the concept of security capacity which measures the strength or effectiveness of an MTD strategy: the security capacity depends on MTD specific parameters and more general system parameters. We apply our framework to two concrete MTD strategies.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

paper_content:

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
---
paper_title: Dynamic Programming for Partially Observable Stochastic Games

paper_content:

We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination or dominated strategies in normal form games. We prove that when applied to finite-horizon POSGs, the algorithm iteratively eliminates very weakly dominated strategies without first forming a normal form representation of the game. For the special case in which agents share the same payoffs, the algorithm can be used to find an optimal solution. We present preliminary empirical results and discuss ways to further exploit POMDP theory in solving POSGs.
---
paper_title: MTD Analysis and evaluation framework in Software Defined Network (MASON)

paper_content:

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.
---
paper_title: Towards a Theory of Moving Target Defense

paper_content:

The static nature of cyber systems gives attackers the advantage of time. Fortunately, a new approach, called the Moving Target Defense (MTD) has emerged as a potential solution to this problem. While promising, there is currently little research to show that MTD systems can work effectively in real systems. In fact, there is no standard definition of what an MTD is, what is meant by attack surface, or metrics to define the effectiveness of such systems. In this paper, we propose an initial theory that will begin to answer some of those questions. The paper defines the key concepts required to formally talk about MTD systems and their basic properties. It also discusses three essential problems of MTD systems, which include the MTD Problem (or how to select the next system configuration), the Adaptation Selection Problem, and the Timing Problem. We then formalize the MTD Entropy Hypothesis, which states that the greater the entropy of the system's configuration, the more effective the MTD system.
---
paper_title: Network function virtualization: Challenges and opportunities for innovations

paper_content:

Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.
---
paper_title: Advancing Software-Defined Networks: A Survey

paper_content:

Having gained momentum from its promise of centralized control over distributed network architectures at bargain costs, software-defined Networking (SDN) is an ever-increasing topic of research. SDN offers a simplified means to dynamically control multiple simple switches via a single controller program, which contrasts with current network infrastructures where individual network operators manage network devices individually. Already, SDN has realized some extraordinary use cases outside of academia with companies, such as Google, AT&T, Microsoft, and many others. However, SDN still presents many research and operational challenges for government, industry, and campus networks. Because of these challenges, many SDN solutions have developed in an ad hoc manner that are not easily adopted by other organizations. Hence, this paper seeks to identify some of the many challenges where new and current researchers can still contribute to the advancement of SDN and further hasten its broadening adoption by network operators.
---
paper_title: A Survey of Securing Networks Using Software Defined Networking

paper_content:

Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of networks ranging from the data center to the core, enterprise, and home. The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.
---
paper_title: TRUFL: Distributed Trust Management Framework in SDN

paper_content:

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow for trust establishment in a larger cloud environment. We propose a distributed trust mechanism — TRUFL to establish and verify trust in SDN. The distributed framework utilizes parallelism in trust management, in effect faster transfer rates and reduced latency compared to centralized trust management. The TRUFL framework scales well with the number of OpenFlow rules when compared to existing research works.
---
paper_title: B4: experience with a globally-deployed software defined wan

paper_content:

We present the design, implementation, and evaluation of B4, a private WAN connecting Google's data centers across the planet. B4 has a number of unique characteristics: i) massive bandwidth requirements deployed to a modest number of sites, ii) elastic traffic demand that seeks to maximize average bandwidth, and iii) full control over the edge servers and network, which enables rate limiting and demand measurement at the edge. These characteristics led to a Software Defined Networking architecture using OpenFlow to control relatively simple switches built from merchant silicon. B4's centralized traffic engineering service drives links to near 100% utilization, while splitting application flows among multiple paths to balance capacity against application priority/demands. We describe experience with three years of B4 production deployment, lessons learned, and areas for future work.
---
paper_title: GENI: A federated testbed for innovative network experiments

paper_content:

GENI, the Global Environment for Networking Innovation, is a distributed virtual laboratory for transformative, at-scale experiments in network science, services, and security. Designed in response to concerns over Internet ossification, GENI is enabling a wide variety of experiments in a range of areas, including clean-slate networking, protocol design and evaluation, distributed service offerings, social network integration, content management, and in-network service deployment. Recently, GENI has been leading an effort to explore the potential of its underlying technologies, SDN and GENI racks, in support of university campus network management and applications. With the concurrent deployment of these technologies on regional and national R&E backbones, this will result in a revolutionary new national-scale distributed architecture, bringing to the entire network the shared, deeply programmable environment that the cloud has brought to the datacenter. This deeply programmable environment will support the GENI research mission and as well as enabling research in a wide variety of application areas.
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: SDN-based solutions for Moving Target Defense network protection

paper_content:

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.
---
paper_title: Snort - Lightweight Intrusion Detection for Networks

paper_content:

Network intrusion detection systems (NIDS) are an important part of any network security architecture. They provide a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected. Commercial NIDS have many differences, but Information Systems departments must face the commonalities that they share such as significant system footprint, complex deployment and high monetary cost. Snort was designed to address these issues.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: DDoS defense using MTD and SDN

paper_content:

Distributed large-scale cyber attacks targeting the availability of computing and network resources still remains a serious threat. In order to limit the effects caused by those attacks and to provide a proactive defense, mitigation should move to the networks of Internet Service Providers. In this context, Moving Target Defense (MTD) is a technique that increases uncertainty due to an ever-changing attack surface. In combination with Software Defined Networking (SDN), MTD has the potential to reduce the effects of a large-scale cyber attack. In this paper, we combine the defense techniques moving- target using Software Defined Networking and investigate their effectiveness. We review current moving-target defense strategies and their applicability in context of large-scale cyber attacks and the networks of Internet Service Providers. Further, we enforce the implementation of moving target defense strategies using Software Defined Networks in a collaborative environment. In particular, we focus on ISPs that cooperate among trusted partners. We found that the effects of a large-scale cyber attack can be significantly reduced using the moving-target defense and Software Defined Networking. Moreover, we show that Software Defined Networking is an appropriate approach to enforce implementation of the moving target defense and thus mitigate the effects caused by large-scale cyber attacks.
---
paper_title: CHAOS: an SDN-based Moving Target Defense System

paper_content:

Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
---
paper_title: The Folk Theorem With Imperfect Public Information

paper_content:

AbstractWe study repeated games in which players observe a public outcome that imperfectly signals the actions played. We provide conditions guaranteeing that any feasible, individually rational payoff vector of the stage game can arise as a perfect equilibrium of the repeated game with sufficiently little discounting. The central condition requires that there exist action profiles with the property that, for any two players, no two deviations—one by each player—give rise to the same probability distribution over public outcomes. The results apply to principal-agent, partnership, oligopoly, and mechanismdesign models, and to one-shot games with transferable utilities.
---
paper_title: Optimal Network Reconfiguration for Software Defined Networks Using Shuffle-Based Online MTD

paper_content:

A Software Defined Network (SDN) provides functionalities for modifying network configurations. To enhance security, Moving Target Defense (MTD) techniques are deployed in the networks to continuously change the attack surface. In this paper, we realize an MTD system by exploiting the SDN functionality to optimally reconfigure the network topology. We introduce a novel problem Shuffle Assignment Problem (SAP), the reconfiguration of a network topology for enhanced security, and we show how to compute the optimal solution for small-sized networks and the near-optimal solution for large-sized networks using a heuristic method. In addition, we propose a shuffle-based online MTD mechanism, which periodically reconfigures the network topology to continuously change the attack surface. This mechanism also selects an optimal countermeasure using our proposed topological distance metric in real-time when an attack is detected. We demonstrate the feasibility and the effectiveness of our proposed solutions through experimental analysis on an SDN testbed and simulations.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: GENI: A federated testbed for innovative network experiments

paper_content:

GENI, the Global Environment for Networking Innovation, is a distributed virtual laboratory for transformative, at-scale experiments in network science, services, and security. Designed in response to concerns over Internet ossification, GENI is enabling a wide variety of experiments in a range of areas, including clean-slate networking, protocol design and evaluation, distributed service offerings, social network integration, content management, and in-network service deployment. Recently, GENI has been leading an effort to explore the potential of its underlying technologies, SDN and GENI racks, in support of university campus network management and applications. With the concurrent deployment of these technologies on regional and national R&E backbones, this will result in a revolutionary new national-scale distributed architecture, bringing to the entire network the shared, deeply programmable environment that the cloud has brought to the datacenter. This deeply programmable environment will support the GENI research mission and as well as enabling research in a wide variety of application areas.
---
paper_title: Mayflies: A Moving Target Defense Framework for Distributed Systems

paper_content:

prevent attackers from gaining control of the system using well established techniques such as; perimeter-based fire walls, redundancy and replications, and encryption. However, given sufficient time and resources, all these methods can be defeated. Moving Target Defense (MTD), is a defensive strategy that aims to reduce the need to continuously fight against attacks by disrupting attackers gain-loss balance. We present Mayflies, a bio-inspired generic MTD framework for distributed systems on virtualized cloud platforms. The framework enables systems designed to defend against attacks for their entire runtime to systems that avoid attacks in time intervals. We discuss the design, algorithms and the implementation of the framework prototype. We illustrate the prototype with a quorum-based Byzantime Fault Tolerant system and report the preliminary results.
---
paper_title: Heartbleed 101

paper_content:

The media furor around the Heartbleed vulnerability was incredible and crossed over from security mailing lists to the national press with remarkable speed. The authors look at this vulnerability in OpenSSL and outline how it was fixed. They also address why the Heartbleed vulnerability was missed for so long.
---
paper_title: Nessi2 simulator for large-scale DDoS attack analysis

paper_content:

The aim of the article is to thoroughly analyze the possibilities and techniques of performing strategic cyberattacks on an internetwork, such as the Internet. The main focus will be on large-scale distributed denial-of-service (DDoS) attacks for three primary reasons. Firstly, they appear to have the biggest infrastructural requirements and require coordinated, simultaneous effort to be effective on the scale desired to impact the majority of a victim country, territory or corporation. Such requirements can only be met with significant resources, often associated with powerful organizations and governments. Secondly, in the most notable cyberattacks of the last few years — the 2007 Estonia attacks and the 2008 South Ossetia attacks — DDoS was the primary asset. Thirdly, in these attacks DDoS has proven to be very powerful and debilitating for the victim. These three reasons provide solid grounds to believe that a strategic, large-scale DDoS attack on core network elements will be the tool of choice in at least some of the future military cyberconflicts.
---
paper_title: CyberVAN: A Cyber Security Virtual Assured Network testbed

paper_content:

In this paper we describe CyberVAN, a Cyber Security Virtual Assured Network testbed. CyberVAN enables speedy and flexible setup of high-fidelity cyber security scenarios to evaluate the effectiveness of both novel existing cyber technologies. CyberVAN provides many features needed by cyber security researchers, developers and practitioners alike, and can be used for both verification and validation purposes. We provide an overview of CyberVAN's functionality and a blueprint of the envisioned roadmap. Currently CyberVAN is available to ARL Cyber Security CRA (Collaborative Research Alliance) members. It is being used to evaluate CRA-developed cyber defense technologies and assess their applicability to the military strategic and tactical network environments.
---
paper_title: MTD CBITS: Moving Target Defense for Cloud-Based IT Systems

paper_content:

The static nature of current IT systems gives attackers the extremely valuable advantage of time, as adversaries can take their time and plan attacks at their leisure. Although cloud infrastructures have increased the automation options for managing IT systems, the introduction of Moving Target Defense (MTD) techniques at the entire IT system level is still very challenging. The core idea of MTD is to make a system change proactively as a means to eliminating the asymmetric advantage the attacker has on time. However, due to the number and complexity of dependencies between IT system components, it is not trivial to introduce proactive changes without breaking the system or severely impacting its performance.
---
paper_title: Investigating the application of moving target defenses to network security

paper_content:

This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker's asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network's configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD system that uses attack indicators to augment adaptation selection. A set of simulation-based experiments show that such an MTD system may in fact be able to reduce an attacker's success likelihood. These results are a preliminary step towards understanding and quantifying the impact of MTDs on computer networks.
---
paper_title: OpenStack: Toward an Open-Source Solution for Cloud Computing

paper_content:

Cloud computing is a quite new concept for which the resources are virtualized, dynamically extended and provided as a service on the Internet. In this paper, we present a comparative study between some of the IaaS (Infrastructure as a Service) commonly used to select the best suited one for deployment and research development in the field of cloud computing. The aim is to provide the computer industry with the opportunity to build a hosting architecture, massively scalable which is completely open source, while overcoming the constraints and the use of proprietary technologies. Then, we present the solution OpenStack retained by the comparative study. We discuss in detail its functional and architectural system. We finish by a discussion of the motivation of our choice of the IaaS solution. General Terms:
---
paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
---
paper_title: SDN-based solutions for Moving Target Defense network protection

paper_content:

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.
---
paper_title: Defending Against Advanced Persistent Threats Using Game-Theory

paper_content:

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.
---
paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
---
paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
---
paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
---
paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
---
paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
---
paper_title: U-TRI: Unlinkability Through Random Identifier for SDN Network

paper_content:

Traffic analysis within switches is threatening the security of large enterprise networks built with SDN. Adversaries are able to monitor all traffic traversing a switch by exploiting just one vulnerability in it and obtain linkage information for further attacking, while administrators have to patch all switches as soon as possible in hope of eliminating the vulnerability in time. Moving Target Defense (MTD) is a novel theory for re-obtaining the upper hand in network defense by dynamically changing attack surfaces of the network. In this paper, we propose U-TRI (Unlinkability Through Random Identifier) as a moving target technique for changing the identifier, which is one of the most vital attack surfaces of traffic privacy, within packet data units. U-TRI employs an independent, hierarchically-structured, periodically and randomly changing identifier to replace the original static data link layer addresses. It also hides all other identifiers in the network and transport layer by obfuscating them. Such a combination of hierarchical random address and obfuscated identity enables U-TRI to provide unlinkable communications among hosts. The result of experiments indicates that U-TRI is capable of defending traffic analysis with very little burdens on network performance.
---
paper_title: Mayflies: A Moving Target Defense Framework for Distributed Systems

paper_content:

prevent attackers from gaining control of the system using well established techniques such as; perimeter-based fire walls, redundancy and replications, and encryption. However, given sufficient time and resources, all these methods can be defeated. Moving Target Defense (MTD), is a defensive strategy that aims to reduce the need to continuously fight against attacks by disrupting attackers gain-loss balance. We present Mayflies, a bio-inspired generic MTD framework for distributed systems on virtualized cloud platforms. The framework enables systems designed to defend against attacks for their entire runtime to systems that avoid attacks in time intervals. We discuss the design, algorithms and the implementation of the framework prototype. We illustrate the prototype with a quorum-based Byzantime Fault Tolerant system and report the preliminary results.
---
paper_title: SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine

paper_content:

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender’s strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.
---
paper_title: MT6D: A Moving Target IPv6 Defense

paper_content:

The Internet Protocol version 6 (IPv6) brings with it a seemingly endless supply of network addresses. It does not, however, solve many of the vulnerabilities that existed in Internet Protocol version 4 (IPv4). In fact, privacy-related crimes in IPv6 are made easier due to the way IPv6 addresses are formed. We developed a Moving Target IPv6 Defense (MT6D) that leverages the immense address space of IPv6. The two goals of MT6D are maintaining user privacy and protecting against targeted network attacks. These goals are achieved by repeatedly rotating the addresses of both the sender and receiver. Address rotation occurs, regardless of the state of ongoing sessions, to prevent an attacker from discovering the identities of the two communicating hosts. Rotating addresses mid-session prevents an attacker from even determining that the same two hosts are communicating. The continuously changing addresses also force an attacker to repeatedly reacquire the target node before he or she can launch a successful network attack. Our proof of concept demonstrates the feasibility of MT6D and its ability to seamlessly bind new IPv6 addresses. We also demonstrate MT6D's ability to rotate addresses mid-session without dropping or renegotiating sessions. Since MT6D operates at the network layer of the protocol stack, it provides a powerful moving target solution that is both platform and application independent.
---
paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
---
paper_title: Moving Target Defense for Web Applications using Bayesian Stackelberg Games

paper_content:

Vulnerabilities in web applications allow hackers to access and/or modify restricted data. Here the hackers have the opportunity to perform reconnaissance so as to gain knowledge about the web application layout before launching an attack, whereas the defender (administrator of the web application) must secure the application even with its potential vulnerabilities. In order to mask such vulnerabilities which are primarily associated with different individual configurations, Moving Target Defense systems were proposed wherein the defender switches between various configurations thereby making it difficult to attack with success, while maintaining a seamless experience for the genuine users. In this paper, we present a way to find effective switching strategies by modeling this ecosystem as a Bayesian Stackelberg game with the administrator as the leader and the hackers as the followers, which as we show succinctly captures various aspects of the Moving Target Defense systems. Furthermore, we propose ways to find the most critical vulnerabilities and the most sensitive attacker types, which are key issues in such scenarios.
---
paper_title: Large-Scale Automated Software Diversity—Program Evolution Redux

paper_content:

The software monoculture favors attackers over defenders, since it makes all target environments appear similar. Code-reuse attacks, for example, rely on target hosts running identical software. Attackers use this assumption to their advantage by automating parts of creating an attack. This article presents large-scale automated software diversification as a means to shore up this vulnerability implied by our software monoculture. Besides describing an industrial-strength implementation of automated software diversity, we introduce methods to objectively measure the effectiveness of diversity in general, and its potential to eliminate code-reuse attacks in particular.
---
paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
---
paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
---
paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
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paper_title: DDoS defense using MTD and SDN

paper_content:

Distributed large-scale cyber attacks targeting the availability of computing and network resources still remains a serious threat. In order to limit the effects caused by those attacks and to provide a proactive defense, mitigation should move to the networks of Internet Service Providers. In this context, Moving Target Defense (MTD) is a technique that increases uncertainty due to an ever-changing attack surface. In combination with Software Defined Networking (SDN), MTD has the potential to reduce the effects of a large-scale cyber attack. In this paper, we combine the defense techniques moving- target using Software Defined Networking and investigate their effectiveness. We review current moving-target defense strategies and their applicability in context of large-scale cyber attacks and the networks of Internet Service Providers. Further, we enforce the implementation of moving target defense strategies using Software Defined Networks in a collaborative environment. In particular, we focus on ISPs that cooperate among trusted partners. We found that the effects of a large-scale cyber attack can be significantly reduced using the moving-target defense and Software Defined Networking. Moreover, we show that Software Defined Networking is an appropriate approach to enforce implementation of the moving target defense and thus mitigate the effects caused by large-scale cyber attacks.
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paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
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paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
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paper_title: MTD CBITS: Moving Target Defense for Cloud-Based IT Systems

paper_content:

The static nature of current IT systems gives attackers the extremely valuable advantage of time, as adversaries can take their time and plan attacks at their leisure. Although cloud infrastructures have increased the automation options for managing IT systems, the introduction of Moving Target Defense (MTD) techniques at the entire IT system level is still very challenging. The core idea of MTD is to make a system change proactively as a means to eliminating the asymmetric advantage the attacker has on time. However, due to the number and complexity of dependencies between IT system components, it is not trivial to introduce proactive changes without breaking the system or severely impacting its performance.
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paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
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paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
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paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
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paper_title: Investigating the application of moving target defenses to network security

paper_content:

This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker's asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network's configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD system that uses attack indicators to augment adaptation selection. A set of simulation-based experiments show that such an MTD system may in fact be able to reduce an attacker's success likelihood. These results are a preliminary step towards understanding and quantifying the impact of MTDs on computer networks.
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paper_title: A survey of techniques for internet traffic classification using machine learning

paper_content:

The research community has begun looking for IP traffic classification techniques that do not rely on `well known? TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.
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paper_title: NetHide: secure and practical network topology obfuscation

paper_content:

Simple path tracing tools such as traceroute allow malicious users to infer network topologies remotely and use that knowledge to craft advanced denial-of-service (DoS) attacks such as Link-Flooding Attacks (LFAs). Yet, despite the risk, most network operators still allow path tracing as it is an essential network debugging tool. ::: ::: In this paper, we present NetHide, a network topology obfuscation framework that mitigates LFAs while preserving the practicality of path tracing tools. The key idea behind NetHide is to formulate network obfuscation as a multi-objective optimization problem that allows for a flexible tradeoff between security (encoded as hard constraints) and usability (encoded as soft constraints). While solving this problem exactly is hard, we show that NetHide can obfuscate topologies at scale by only considering a subset of the candidate solutions and without reducing obfuscation quality. In practice, NetHide obfuscates the topology by intercepting and modifying path tracing probes directly in the data plane. We show that this process can be done at line-rate, in a stateless fashion, by leveraging the latest generation of programmable network devices. ::: ::: We fully implemented NetHide and evaluated it on realistic topologies. Our results show that NetHide is able to obfuscate large topologies (> 150 nodes) while preserving near-perfect debugging capabilities. In particular, we show that operators can still precisely trace back > 90% of link failures despite obfuscation.
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paper_title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

paper_content:

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
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paper_title: Empirical Game-Theoretic Analysis for Moving Target Defense

paper_content:

The effectiveness of a moving target defense depends on how it is deployed through specific system operations over time, and how attackers may respond to this deployment. We define a generic cyber-defense scenario, and examine the interplay between attack and defense strategies using empirical game-theoretic techniques. In this approach, the scenario is defined procedurally by a simulator, and data derived from systematic simulation is used to induce a game model. We explore a space of 72 game instances, defined by differences in agent objectives, attack cost, and ability of the defender to detect attack actions. We observe a range of qualitative strategic behaviors, which vary in clear patterns across environmental conditions. In particular, we find that the efficacy of deterrent defense is critically sensitive to detection capability, and in the absence of perfect detection the defender is often driven to proactive moving-target actions.
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paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
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paper_title: Dynamic Game based Security framework in SDN-enabled Cloud Networking Environments

paper_content:

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.
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paper_title: SDN-based solutions for Moving Target Defense network protection

paper_content:

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.
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paper_title: Defending Against Advanced Persistent Threats Using Game-Theory

paper_content:

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.
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paper_title: Frequency-minimal moving target defense using software-defined networking

paper_content:

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.
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paper_title: Mitigating Crossfire Attacks Using SDN-Based Moving Target Defense

paper_content:

Recent research demonstrated that software defined networking (SDN) can be leveraged to enable moving target defense (MTD) to mitigate distributed denial of service (DDoS) attacks. The network states are continuously changed in MTD by effectively collecting information from the network and enforcing certain security measures on the fly in order to deceive the attackers. Being motivated from the success of SDN-based maneuvering, this work targets an emerging type of DDoS attacks, called Crossfire, and proposes an SDN-based MTD mechanism to defend against such attacks. We analyze Crossfire attack planning and utilize the analyzed results to develop the defense mechanism which in turn reorganize the routes in such a way that the congested links are avoided during packet forwarding. The detection and mitigation techniques are implemented using Mininet emulator and Floodlight SDN controller. The evaluation results show that the route mutation can effectively reduce the congestion in the targeted links without making any major disruption on network services.
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paper_title: MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

paper_content:

Present attack methods can make state-of-the-art classification systems based on deep neural networks mis-classify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for ‘boosting’ the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image at test time, a constituent network is randomly selected based on a mixed policy. To obtain this policy, we formulate the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that our approach MTDeep, reduces misclassification on perturbed images for various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms alone. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.
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paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
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paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
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paper_title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

paper_content:

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
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paper_title: U-TRI: Unlinkability Through Random Identifier for SDN Network

paper_content:

Traffic analysis within switches is threatening the security of large enterprise networks built with SDN. Adversaries are able to monitor all traffic traversing a switch by exploiting just one vulnerability in it and obtain linkage information for further attacking, while administrators have to patch all switches as soon as possible in hope of eliminating the vulnerability in time. Moving Target Defense (MTD) is a novel theory for re-obtaining the upper hand in network defense by dynamically changing attack surfaces of the network. In this paper, we propose U-TRI (Unlinkability Through Random Identifier) as a moving target technique for changing the identifier, which is one of the most vital attack surfaces of traffic privacy, within packet data units. U-TRI employs an independent, hierarchically-structured, periodically and randomly changing identifier to replace the original static data link layer addresses. It also hides all other identifiers in the network and transport layer by obfuscating them. Such a combination of hierarchical random address and obfuscated identity enables U-TRI to provide unlinkable communications among hosts. The result of experiments indicates that U-TRI is capable of defending traffic analysis with very little burdens on network performance.
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paper_title: Mayflies: A Moving Target Defense Framework for Distributed Systems

paper_content:

prevent attackers from gaining control of the system using well established techniques such as; perimeter-based fire walls, redundancy and replications, and encryption. However, given sufficient time and resources, all these methods can be defeated. Moving Target Defense (MTD), is a defensive strategy that aims to reduce the need to continuously fight against attacks by disrupting attackers gain-loss balance. We present Mayflies, a bio-inspired generic MTD framework for distributed systems on virtualized cloud platforms. The framework enables systems designed to defend against attacks for their entire runtime to systems that avoid attacks in time intervals. We discuss the design, algorithms and the implementation of the framework prototype. We illustrate the prototype with a quorum-based Byzantime Fault Tolerant system and report the preliminary results.
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paper_title: SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine

paper_content:

A “noisy-rich” (NR) cyber-attacker (Lippmann et al. 2012) is one who tries all available vulnerabilities until he or she successfully compromises the targeted network. We develop an adversarial foundation, based on Stackelberg games, for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph. We develop a mechanism by which the network can be modified by the defender to induce deception by placing honey nodes and apparent vulnerabilities into the network to minimize the expected impact of the NR-attacker’s attacks (according to multiple measures of impact). We also consider the case where the adversary learns from blocked attacks using reinforcement learning. We run detailed experiments with real network data (but with simulated attack data) and show that Stackelberg Honey-based Adversarial Reasoning Engine performs very well, even when the adversary deviates from the initial assumptions made about his or her behavior. We also develop a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender. We propose two stopping policies for the defender: Stop Upon Detection allows the attacker to learn about the defender’s strategy and (according to our experiments) leads to significant damage in the long run, whereas Stop After Delay allows the defender to introduce greater uncertainty into the attacker, leading to better defendability in the long run.
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paper_title: MT6D: A Moving Target IPv6 Defense

paper_content:

The Internet Protocol version 6 (IPv6) brings with it a seemingly endless supply of network addresses. It does not, however, solve many of the vulnerabilities that existed in Internet Protocol version 4 (IPv4). In fact, privacy-related crimes in IPv6 are made easier due to the way IPv6 addresses are formed. We developed a Moving Target IPv6 Defense (MT6D) that leverages the immense address space of IPv6. The two goals of MT6D are maintaining user privacy and protecting against targeted network attacks. These goals are achieved by repeatedly rotating the addresses of both the sender and receiver. Address rotation occurs, regardless of the state of ongoing sessions, to prevent an attacker from discovering the identities of the two communicating hosts. Rotating addresses mid-session prevents an attacker from even determining that the same two hosts are communicating. The continuously changing addresses also force an attacker to repeatedly reacquire the target node before he or she can launch a successful network attack. Our proof of concept demonstrates the feasibility of MT6D and its ability to seamlessly bind new IPv6 addresses. We also demonstrate MT6D's ability to rotate addresses mid-session without dropping or renegotiating sessions. Since MT6D operates at the network layer of the protocol stack, it provides a powerful moving target solution that is both platform and application independent.
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paper_title: Software diversity: Security, Entropy and Game Theory

paper_content:

Although many have recognized the risks of software monocultures, it is not currently clear how much and what kind of diversity would be needed to address these risks. Here we attempt to provide insight into this issue using a simple model of hosts and vulnerabilities connected in a bipartite graph. We use this graph to compute diversity metrics as Renyi entropy and to formulate an anti-coordination game to understand why computer host owners would choose to diversify. Since security isn't the only factor considered when choosing software in the real world, we propose a slight variation of the popular security wargame Capture the Flag that can serve as a testbed for understanding the utility of diversity as a defense strategy.
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paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
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paper_title: Moving Target Defense for Web Applications using Bayesian Stackelberg Games

paper_content:

Vulnerabilities in web applications allow hackers to access and/or modify restricted data. Here the hackers have the opportunity to perform reconnaissance so as to gain knowledge about the web application layout before launching an attack, whereas the defender (administrator of the web application) must secure the application even with its potential vulnerabilities. In order to mask such vulnerabilities which are primarily associated with different individual configurations, Moving Target Defense systems were proposed wherein the defender switches between various configurations thereby making it difficult to attack with success, while maintaining a seamless experience for the genuine users. In this paper, we present a way to find effective switching strategies by modeling this ecosystem as a Bayesian Stackelberg game with the administrator as the leader and the hackers as the followers, which as we show succinctly captures various aspects of the Moving Target Defense systems. Furthermore, we propose ways to find the most critical vulnerabilities and the most sensitive attacker types, which are key issues in such scenarios.
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paper_title: Large-Scale Automated Software Diversity—Program Evolution Redux

paper_content:

The software monoculture favors attackers over defenders, since it makes all target environments appear similar. Code-reuse attacks, for example, rely on target hosts running identical software. Attackers use this assumption to their advantage by automating parts of creating an attack. This article presents large-scale automated software diversification as a means to shore up this vulnerability implied by our software monoculture. Besides describing an industrial-strength implementation of automated software diversity, we introduce methods to objectively measure the effectiveness of diversity in general, and its potential to eliminate code-reuse attacks in particular.
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paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
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paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
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paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
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paper_title: CHAOS: an SDN-based Moving Target Defense System

paper_content:

Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
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paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
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paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
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paper_title: Game Theoretic Approaches to Attack Surface Shifting

paper_content:

A software system’s attack surface is the set of ways in which the system can be attacked. In our prior work, we introduced an attack surface measurement and reduction method to mitigate a software system’s security risk (Manadhata, An attack surface metric, Ph.D. thesis, Carnegie Mellon University, 2008; Manadhata and Wing, IEEE Trans. Softw. Eng. 37:371–386, 2011). In this paper, we explore the use of attack surface shifting in the moving target defense approach. We formalize the notion of shifting the attack surface and introduce a method to quantify the shift. We cast the moving target defense approach as a security-usability trade-off and introduce a two-player stochastic game model to determine an optimal moving target defense strategy. A system’s defender can use our game theoretic approach to optimally shift and reduce the system’s attack surface.
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paper_title: MTD CBITS: Moving Target Defense for Cloud-Based IT Systems

paper_content:

The static nature of current IT systems gives attackers the extremely valuable advantage of time, as adversaries can take their time and plan attacks at their leisure. Although cloud infrastructures have increased the automation options for managing IT systems, the introduction of Moving Target Defense (MTD) techniques at the entire IT system level is still very challenging. The core idea of MTD is to make a system change proactively as a means to eliminating the asymmetric advantage the attacker has on time. However, due to the number and complexity of dependencies between IT system components, it is not trivial to introduce proactive changes without breaking the system or severely impacting its performance.
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paper_title: An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

paper_content:

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.
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paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
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paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
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paper_title: Investigating the application of moving target defenses to network security

paper_content:

This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker's asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network's configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD system that uses attack indicators to augment adaptation selection. A set of simulation-based experiments show that such an MTD system may in fact be able to reduce an attacker's success likelihood. These results are a preliminary step towards understanding and quantifying the impact of MTDs on computer networks.
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paper_title: NetHide: secure and practical network topology obfuscation

paper_content:

Simple path tracing tools such as traceroute allow malicious users to infer network topologies remotely and use that knowledge to craft advanced denial-of-service (DoS) attacks such as Link-Flooding Attacks (LFAs). Yet, despite the risk, most network operators still allow path tracing as it is an essential network debugging tool. ::: ::: In this paper, we present NetHide, a network topology obfuscation framework that mitigates LFAs while preserving the practicality of path tracing tools. The key idea behind NetHide is to formulate network obfuscation as a multi-objective optimization problem that allows for a flexible tradeoff between security (encoded as hard constraints) and usability (encoded as soft constraints). While solving this problem exactly is hard, we show that NetHide can obfuscate topologies at scale by only considering a subset of the candidate solutions and without reducing obfuscation quality. In practice, NetHide obfuscates the topology by intercepting and modifying path tracing probes directly in the data plane. We show that this process can be done at line-rate, in a stateless fashion, by leveraging the latest generation of programmable network devices. ::: ::: We fully implemented NetHide and evaluated it on realistic topologies. Our results show that NetHide is able to obfuscate large topologies (> 150 nodes) while preserving near-perfect debugging capabilities. In particular, we show that operators can still precisely trace back > 90% of link failures despite obfuscation.
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paper_title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

paper_content:

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
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paper_title: Multistage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis

paper_content:

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multistage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty and very large strategy spaces. We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity. Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions. Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies. Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
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paper_title: Empirical Game-Theoretic Analysis for Moving Target Defense

paper_content:

The effectiveness of a moving target defense depends on how it is deployed through specific system operations over time, and how attackers may respond to this deployment. We define a generic cyber-defense scenario, and examine the interplay between attack and defense strategies using empirical game-theoretic techniques. In this approach, the scenario is defined procedurally by a simulator, and data derived from systematic simulation is used to induce a game model. We explore a space of 72 game instances, defined by differences in agent objectives, attack cost, and ability of the defender to detect attack actions. We observe a range of qualitative strategic behaviors, which vary in clear patterns across environmental conditions. In particular, we find that the efficacy of deterrent defense is critically sensitive to detection capability, and in the absence of perfect detection the defender is often driven to proactive moving-target actions.
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paper_title: Intriguing properties of neural networks

paper_content:

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. ::: First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. ::: Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.
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paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
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paper_title: Optimal Network Reconfiguration for Software Defined Networks Using Shuffle-Based Online MTD

paper_content:

A Software Defined Network (SDN) provides functionalities for modifying network configurations. To enhance security, Moving Target Defense (MTD) techniques are deployed in the networks to continuously change the attack surface. In this paper, we realize an MTD system by exploiting the SDN functionality to optimally reconfigure the network topology. We introduce a novel problem Shuffle Assignment Problem (SAP), the reconfiguration of a network topology for enhanced security, and we show how to compute the optimal solution for small-sized networks and the near-optimal solution for large-sized networks using a heuristic method. In addition, we propose a shuffle-based online MTD mechanism, which periodically reconfigures the network topology to continuously change the attack surface. This mechanism also selects an optimal countermeasure using our proposed topological distance metric in real-time when an attack is detected. We demonstrate the feasibility and the effectiveness of our proposed solutions through experimental analysis on an SDN testbed and simulations.
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paper_title: A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses

paper_content:

Moving target defenses based on dynamic platforms have been proposed as a way to make systems more resistant to attacks by changing the properties of the deployed platforms. Unfortunately, little work has been done on discerning effective strategies for the utilization of these systems, instead relying on two generally false premises: simple randomization leads to diversity and platforms are independent. In this paper, we study the strategic considerations of deploying a dynamic platform system by specifying a relevant threat model and applying game theory and statistical analysis to discover optimal usage strategies. We show that preferential selection of platforms based on optimizing platform diversity approaches the statistically optimal solution and significantly outperforms simple randomization strategies. Counter to popular belief, this deterministic strategy leverages fewer platforms than may be generally available, which increases system security.
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paper_title: NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems

paper_content:

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.
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paper_title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

paper_content:

The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.
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paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
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paper_title: Brew: A Security Policy Analysis Framework for Distributed SDN-Based Cloud Environments

paper_content:

The ease of programmability in Software-Defined Networking (SDN) makes it a great platform implementation of various initiatives that involve application deployment, dynamic topology changes, and decentralized network management in a multi-tenant data center environment. However, implementing security solutions in such an environment is fraught with policy conflicts and consistency issues with the hardness of this problem being affected by the distribution scheme for the SDN controllers. In this paper we present Brew, a security policy analysis framework implemented on an OpenDaylight SDN controller, that has comprehensive conflict detection and resolution modules to ensure that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free security policy implementation and preventing information leakage. We present techniques for global prioritization of flow rules in a decentralized environment, extend firewall rule conflict classification from a traditional environment to SDN flow rule conflicts by recognizing and classifying conflicts stemming from cross-layer conflicts and provide strategies for unassisted resolution of these conflicts. Alternately, if administrator input is desired to resolve conflicts, a novel visualization scheme is implemented to help the administrators view the conflicts graphically. We demonstrate the correctness, feasibility and scalability of our framework through a proof-of-concept prototype.
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paper_title: Predictability-oriented defense against adaptive adversaries

paper_content:

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learner's feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.
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paper_title: Openflow random host mutation: transparent moving target defense using software defined networking

paper_content:

Static configurations serve great advantage for adversaries in discovering network targets and launching attacks. Identifying active IP addresses in a target domain is a precursory step for many attacks. Frequently changing hosts' IP addresses is a novel proactive moving target defense (MTD) that hides network assets from external/internal scanners. In this paper, we use OpenFlow to develop a MTD architecture that transparently mutates host IP addresses with high unpredictability and rate, while maintaining configuration integrity and minimizing operation overhead. The presented technique is called OpenFlow Random Host Mutation (OF-RHM) in which the OpenFlow controller frequently assigns each host a random virtual IP that is translated to/from the real IP of the host. The real IP remains untouched, so IP mutation is completely transparent for end-hosts. Named hosts are reachable via the virtual IP addresses acquired via DNS, but real IP addresses can be only reached by authorized entities. Our implementation and evaluation show that OF-RHM can effectively defend against stealthy scanning, worm propagation, and other scanning-based attack.
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paper_title: A Moving Target Defense Approach to Disrupting Stealthy Botnets

paper_content:

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
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paper_title: Multiple OS rotational environment an implemented Moving Target Defense

paper_content:

Cyber-attacks continue to pose a major threat to existing critical infrastructure. Although suggestions for defensive strategies abound, Moving Target Defense (MTD) has only recently gained attention as a possible solution for mitigating cyber-attacks. The current work proposes a MTD technique that provides enhanced security through a rotation of multiple operating systems. The MTD solution developed in this research utilizes existing technology to provide a feasible dynamic defense solution that can be deployed easily in a real networking environment. In addition, the system we developed was tested extensively for effectiveness using CORE Impact Pro (CORE), Nmap, and manual penetration tests. The test results showed that platform diversity and rotation offer improved security. In addition, the likelihood of a successful attack decreased proportionally with time between rotations.
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paper_title: CHAOS: an SDN-based Moving Target Defense System

paper_content:

Moving target defense (MTD) has provided a dynamic and proactive network defense to reduce or move the attack surface that is available for exploitation. However, traditional network is difficult to realize dynamic and active security defense effectively and comprehensively. Software-defined networking (SDN) points out a brand-new path for building dynamic and proactive defense system. In this paper, we propose CHAOS, an SDN-based MTD system. Utilizing the programmability and flexibility of SDN, CHAOS obfuscates the attack surface including host mutation obfuscation, ports obfuscation, and obfuscation based on decoy servers, thereby enhancing the unpredictability of the networking environment. We propose the Chaos Tower Obfuscation (CTO) method, which uses the Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and define expected connection and unexpected connection. Moreover, we develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. We design and implement CHAOS as an application of SDN controller. Our approach makes it very easy to realize moving target defense in networks. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
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paper_title: Scalable Security Model Generation and Analysis Using k-importance Measures

paper_content:

Attack representation models (ARMs) (such as attack graphs, attack trees) can be used to model and assess security of a networked system. To do this, one must generate an ARM. However, generation and evaluation of the ARM suffer from a scalability problem when the size of the networked system is very large (e.g., 10,000 computer hosts in the network with a complex network topology). The main reason is that computing all possible attack scenarios to cover all aspects of an attack results in a state space explosion. One idea is to use only important hosts and vulnerabilities in the networked system to generate and evaluate security. We propose to use k-importance measures to generate a two-layer hierarchical ARM that will improve the scalability of model generation and security evaluation computational complexities. We use k 1 number of important hosts based on network centrality measures and k 2 number of significant vulnerabilities of hosts using host security metrics. We show that an equivalent security analysis can be achieved using our approach (using k-importance measures), compared to an exhaustive search.
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paper_title: A Game-Theoretic Approach to IP Address Randomization in Decoy-Based Cyber Defense

paper_content:

Networks of decoy nodes protect cyber systems by distracting and misleading adversaries. Decoy defenses can be further enhanced by randomizing the space of node IP addresses, thus preventing an adversary from identifying and blacklisting decoy nodes over time. The decoy-based defense results in a time-varying interaction between the adversary, who attempts to identify and target real nodes, and the system, which deploys decoys and randomizes the address space in order to protect the identity of the real node. In this paper, we present a game-theoretic framework for modeling the strategic interaction between an external adversary and a network of decoy nodes. Our framework consists of two components. First, we model and study the interaction between the adversary and a single decoy node. We analyze the case where the adversary attempts to identify decoy nodes by examining the timing of node responses, as well as the case where the adversary identifies decoys via differences in protocol implementations between decoy and real nodes. Second, we formulate games with an adversary who attempts to find a real node in a network consisting of real and decoy nodes, where the time to detect whether a node is real or a decoy is derived from the equilibria of the games in first component. We derive the optimal policy of the system to randomize the IP address space in order to avoid detection of the real node, and prove that there is a unique threshold-based Stackelberg equilibrium for the game. Through simulation study, we find that the game between a single decoy and an adversary mounting timing-based attacks has a pure-strategy Nash equilibrium, while identification of decoy nodes via protocol implementation admits only mixed-strategy equilibria.
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paper_title: Towards Cost-Effective Moving Target Defense Against DDoS and Covert Channel Attacks

paper_content:

Traditionally, network and system configurations are static. Attackers have plenty of time to exploit the system's vulnerabilities and thus they are able to choose when to launch attacks wisely to maximize the damage. An unpredictable system configuration can significantly lift the bar for attackers to conduct successful attacks. Recent years, moving target defense (MTD) has been advocated for this purpose. An MTD mechanism aims to introduce dynamics to the system through changing its configuration continuously over time, which we call adaptations. Though promising, the dynamic system reconfiguration introduces overhead to the applications currently running in the system. It is critical to determine the right time to conduct adaptations and to balance the overhead afforded and the security levels guaranteed. This problem is known as the MTD timing problem. Little prior work has been done to investigate the right time in making adaptations. In this paper, we take the first step to both theoretically and experimentally study the timing problem in moving target defenses. For a broad family of attacks including DDoS attacks and cloud covert channel attacks, we model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to make adaptations with the objective of minimizing the long-term cost rate. In our experiments, both DDoS attacks and cloud covert channel attacks are studied. Simulations based on real network traffic traces are conducted and we demonstrate that our proposed algorithm outperforms known adaptation schemes.
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paper_title: Moving Target Network Defense Effectiveness Evaluation Based on Change-Point Detection

paper_content:

In order to evaluate the effectiveness of moving target network defense, a dynamic effectiveness evaluation approach based on change-point detection is presented. Firstly, the concept of multilayer network resource graph is defined, which helps establish the relationship between the change of resource vulnerability and the transfer of network node state. Secondly, a change-point detection and standardized measurement algorithm is proposed. Consequently, it improves the efficiency of evaluation by measuring the change-point dynamically and enhancing the accuracy of evaluation based on multilayer network resource graph. What’s more, in order to evaluate the defense effectiveness comprehensively, defense cost and benefits are set as evaluation indicators. Finally, experimental analysis, represented by MT6D and DNAT, proves the feasibility of the proposed evaluation method and the accuracy of the evaluation results.
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paper_title: A Quantitative Framework for Moving Target Defense Effectiveness Evaluation

paper_content:

Static defense has proven to be a brittle mechanism for defending against cyber attack. Despite this, proactive defensive measures have not been widely deployed. This is because flexible proactive defensive measures such as Moving Target Defense (MTD) have as much potential to interfere with a network's ability to support the mission as they do to defend the network. In this paper we introduce an approach to defining and measuring MTD effects applied in a network environment to help guide MTD deployment decisions that successfully balance the potential security benefits of MTD deployment against the potential productivity costs.
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paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
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paper_title: Moving Target Defense for the Placement of Intrusion Detection Systems in the Cloud

paper_content:

A lot of software systems are deployed in the cloud. Owing to realistic demands for an early product launch, oftentimes there are vulnerabilities that are present in these deployed systems (or eventually found out). The cloud service provider can find and leverage this knowledge about known vulnerabilities and the underlying communication network topology of the system to position network and host-based Intrusion Detection Systems (IDS) that can effectively detect attacks. Unfortunately, deploying IDS on each host and network interface impacts the performance of the overall system. Thus, in this paper, we address the problem of placing a limited number of IDS by using the concept of Moving Target Defense (MTD). In essence, we propose an MTD system that allows a defender to shift the detection surfaces and strategically switch among the different IDS placement configurations in each round. To find a secure switching strategy, we (1) formulate the problem of placing a limited number of IDS systems in a large cloud network as a Stackelberg Game between the cloud administrator and an (external or stealthy) attacker, (2) design scalable methods to find the optimal strategies for switching IDS placements at the start of each round, and (3) formally define the problem of identifying the most critical vulnerability that should be fixed, and propose a solution for it. We compare the strategy generated by our method to other state-of-the-art strategies, showcasing the effectiveness and scalability of our method for real-world scenarios.
---
paper_title: Measuring ISP topologies with rocketfuel

paper_content:

To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to directly measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, exploiting properties of IP routing to eliminate redundant measurements, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the inter-domain peering structure. As part of this work, we release our maps to the community.
---
paper_title: Performance Modeling of Moving Target Defenses

paper_content:

In recent years, Moving Target Defense (MTD) has emerged as a potential game changer in the security landscape, due to its potential to create asymmetric uncertainty that favors the defender. Many different MTD techniques have then been proposed, each addressing an often very specific set of attack vectors. Despite the huge progress made in this area, there are still some critical gaps with respect to the analysis and quantification of the cost and benefits of deploying MTD techniques. In fact, common metrics to assess the performance of these techniques are still lacking and most of them tend to assess their performance in different and often incompatible ways. This paper addresses these gaps by proposing a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for the determination of the highest possible reconfiguration rate, and thus smallest probability of attacker's success, that meets performance and stability constraints. Finally, we present an experimental validation of the proposed approach.
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paper_title: VeriFlow: verifying network-wide invariants in real time

paper_content:

Networks are complex and prone to bugs. Existing tools that check network configuration files and the data-plane state operate offline at timescales of seconds to hours, and cannot detect or prevent bugs as they arise. ::: ::: Is it possible to check network-wide invariants in real time, as the network state evolves? The key challenge here is to achieve extremely low latency during the checks so that network performance is not affected. In this paper, we present a design, VeriFlow, which achieves this goal. VeriFlow is a layer between a software-defined networking controller and network devices that checks for network-wide invariant violations dynamically as each forwarding rule is inserted, modified or deleted. VeriFlow supports analysis over multiple header fields, and an API for checking custom invariants. Based on a prototype implementation integrated with the NOX OpenFlow controller, and driven by a Mininet OpenFlow network and Route Views trace data, we find that VeriFlow can perform rigorous checking within hundreds of microseconds per rule insertion or deletion.
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paper_title: Automated Effectiveness Evaluation of Moving Target Defenses: Metrics for Missions and Attacks

paper_content:

In this paper, we describe the results of several experiments designed to test two dynamic network moving target defenses against a propagating data exfiltration attack. We designed a collection of metrics to assess the costs to mission activities and the benefits in the face of attacks and evaluated the impacts of the moving target defenses in both areas. Experiments leveraged Siege's Cyber-Quantification Framework to automatically provision the networks used in the experiment, install the two moving target defenses, collect data, and analyze the results. We identify areas in which the costs and benefits of the two moving target defenses differ, and note some of their unique performance characteristics.
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paper_title: Assessing the Effectiveness of Moving Target Defenses Using Security Models

paper_content:

Cyber crime is a developing concern, where criminals are targeting valuable assets and critical infrastructures within networked systems, causing a severe socio-economic impact on enterprises and individuals. Adopting moving target defense (MTD) helps thwart cyber attacks by continuously changing the attack surface. There are numerous MTD techniques proposed in various domains (e.g., virtualized network, wireless sensor network), but there is still a lack of methods to assess and compare the effectiveness of them. Security models, such as an attack graph (AG), provide a formal method of analyzing the security, but incorporating MTD techniques in those security models has not been studied. In this paper, we incorporate MTD techniques into a security model, namely a hierarchical attack representation model (HARM), to assess the effectiveness of them. In addition, we use importance measures (IMs) for deploying MTD techniques to enhance the scalability. Finally, we compare the scalability of AG and HARM when deploying MTD techniques, as well as changes in performance and security in our experiments.
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paper_title: Taxonomy of conflicts in network security policies

paper_content:

Network security polices are essential elements in Internet security devices that provide traffic filtering, integrity, confidentiality, and authentication. Network security perimeter devices such as firewalls, IPSec, and IDS/IPS devices operate based on locally configured policies. However, configuring network security policies remains a complex and error-prone task due to rule dependency semantics and the interaction between policies in the network. This complexity is likely to increase as the network size increases. A successful deployment of a network security system requires global analysis of policy configurations of all network security devices in order to avoid policy conflicts and inconsistency. Policy conflicts may cause serious security breaches and network vulnerability such as blocking legitimate traffic, permitting unwanted traffic, and insecure data transmission. This article presents a comprehensive classification of security policy conflicts that might potentially exist in a single security device (intrapolicy conflicts) or between different network devices (interpolicy conflicts) in enterprise networks. We also show the high probability of creating such conflicts even by expert system administrators and network practitioners.
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paper_title: Tools for Generating and Analyzing Attack Graphs

paper_content:

Attack graphs depict ways in which an adversary exploits system vulnerabilities to achieve a desired state. System administrators use attack graphs to determine how vulnerable their systems are and to determine what security measures to deploy to defend their systems. In this paper, we present details of an example to illustrate how we specify and analyze network attack models. We take these models as input to our attack graph tools to generate attack graphs automatically and to analyze system vulnerabilities. While we have published our generation and analysis algorithms in earlier work, the presentation of our example and toolkit is novel to this paper.
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paper_title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

paper_content:

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
---
paper_title: Probabilistic Performance Analysis of Moving Target and Deception Reconnaissance Defenses

paper_content:

Deception and moving target reconnaissance defenses are techniques that attempt to invalidate information an attacker attempts to gather. Deception defenses attempt to mislead attackers performing network reconnaissance, while moving target defenses seek to make it more difficult for the attacker to predict the state of their target by dynamically altering what the attacker sees. Although the deployment of reconnaissance defenses can be effective, there are nontrivial administration costs associated with their configuration and maintenance. As a result, understanding under the circumstances these defenses are effective and efficient is important. This paper introduces probabilistic models for reconnaissance defenses to provide deeper understanding of the theoretical effect these strategies and their parameters have for cyber defense. The models quantify the success of attackers under various conditions, such as network size, deployment of size, and number of vulnerable computers. This paper provides a probabilistic interpretation for the performance of honeypots, for deception, and network address shuffling, for moving target, and their effect in concert. The models indicate that a relatively small number of deployed honeypots can provide an effective defense strategy, often better than movement alone. Furthermore, the models confirm the intuition that that combining, or layering, defense mechanisms provide the largest impact to attacker success while providing a quantitative analysis of the improvement and parameters of each strategy.
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paper_title: Software Defined Stochastic Model for Moving Target Defense

paper_content:

Moving Target Defense (MTD) has emerged as a good solution to deal with dynamic attack surface. The goal is to make it difficult for an attacker to exploit network resources. But it is challenging to provide zero downtime guarantees when performing network rearrangement or when a physical host acts as a single point of failure for virtual servers. In this paper, we introduce Software Defined Networking (SDN) based continuous time modeling techniques to perform virtual machine migration and MTD techniques while maintaining high service availability and system security. This solution will not only increase attackers uncertainty but will also provide low downtime and high availability guarantee for the network.
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paper_title: MTD Analysis and evaluation framework in Software Defined Network (MASON)

paper_content:

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.
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paper_title: Security policy checking in distributed SDN based clouds

paper_content:

Separation of network control from devices in Software Defined Network (SDN) allows for centralized implementation and management of security policies in a cloud computing environment. The ease of programmability also makes SDN a great platform implementation of various initiatives that involve application deployment, dynamic topology changes, and decentralized network management in a multi-tenant data center environment. Dynamic change of network topology, or host reconfiguration in such networks might require corresponding changes to the flow rules in the SDN based cloud environment. Verifying adherence of these new flow policies in the environment to the organizational security policies and ensuring a conflict free environment is especially challenging. In this paper, we extend the work on rule conflicts from a traditional environment to an SDN environment, introducing a new classification to describe conflicts stemming from cross-layer conflicts. Our framework ensures that in any SDN based cloud, flow rules do not have conflicts at any layer; thereby ensuring that changes to the environment do not lead to unintended consequences. We demonstrate the correctness, feasibility and scalability of our framework through a proof-of-concept prototype.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Defending Against Advanced Persistent Threats Using Game-Theory

paper_content:

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.
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paper_title: GADAPT: A Sequential Game-Theoretic Framework for Designing Defense-in-Depth Strategies Against Advanced Persistent Threats

paper_content:

We present a dynamic game framework to model and design defense strategies for advanced persistent threats APTs. The model is based on a sequence of nested finite two-person zero-sum games, in which the APT is modeled as the attempt to get through multiple protective shells of a system towards conquering the target located in the center of the infrastructure. In each stage, a sub-game captures the attack and defense interactions between two players, and its outcome determines the security level and the resilience against penetrations as well as the structure of the game in the next stage. By construction, interdependencies between protections at multiple stages are automatically accounted for by the dynamic game. The game model provides an analysis and design framework to develop effective protective layers and strategic defense-in-depth strategies against APTs. We discuss a few closed form solutions of our sequential APT-games, upon which design problems can be formulated to optimize the quality of security QoS across several layers. Numerical experiments are conducted in this work to corroborate our results.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---
paper_title: Optimal Strategy Selection for Moving Target Defense Based on Markov Game

paper_content:

With the evolution of the research on network moving target defense (MTD), the selection of optimal strategy has become one of the key problems in current research. Directed to the problem of the improper defensive strategy selection caused by inaccurately characterizing the attack and defense game in MTD, optimal strategy selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal strategy selection. After comprehensively analyzing the impact of defense cost and defense benefit on the strategy selection, an optimal strategy selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal strategy selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed strategy optimal selection approach.
---
paper_title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

paper_content:

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
---
paper_title: Multistage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis

paper_content:

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multistage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty and very large strategy spaces. We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity. Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions. Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies. Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
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paper_title: SDN based Scalable MTD solution in Cloud Network

paper_content:

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
---
paper_title: Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

paper_content:

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.
---

Title: A Survey of Moving Target Defenses for Network Security

Section 1: INTRODUCTION
Description 1: This section introduces the context and motivation for using Moving Target Defense (MTD) in network security, highlighting the limitations of traditional defense mechanisms and the advantages of MTD.

Section 2: BACKGROUND AND RELATED WORK
Description 2: This section provides background information on attack processes, traditional defense methods, and survey related works in the field of MTD. It also discusses databases and representation methods for modeling known attacks.

Section 3: MOVING TARGET DEFENSE TECHNIQUES
Description 3: This section categorizes various MTD techniques based on what to switch, when to switch, and how to switch. It describes different surfaces that can be shifted and the methodologies for implementing these shifts.

Section 4: IMPLEMENTATION OF MOVING TARGET DEFENSES
Description 4: This section discusses the practical implementation of various MTDs using technologies like Software Defined Networking (SDN) and Network Function Virtualization (NFV). It provides case studies and highlights industry and research testbeds used for evaluation.

Section 5: EVALUATING THE EFFECTIVENESS OF MOVING TARGET DEFENSES
Description 5: This section presents qualitative and quantitative metrics to evaluate the effectiveness of MTDs. It elaborates on the security and performance considerations, and how these metrics can be measured in practice.

Section 6: RESEARCH OPPORTUNITIES
Description 6: This section highlights potential research directions and unexplored areas within MTDs. It discusses future challenges and suggests avenues for further investigation to improve the effectiveness and practicality of MTDs.

Section 7: CONCLUSION
Description 7: This section summarizes the key findings of the survey, discussing the importance of MTD techniques, their implementation challenges, and the benefits of using comprehensive qualitative and quantitative metrics for evaluation.