Application of Data Mining Techniques for Information Security in a Cloud: A Survey
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paper_title: Analysis of Firewall Policy Rules Using Data Mining Techniques

paper_content:

Firewall is the de facto core technology of today's network security and defense. However, the management of firewall rules has been proven to be complex, error-prone, costly and inefficient for many large-networked organizations. These firewall rules are mostly custom-designed and hand-written thus in constant need for tuning and validation, due to the dynamic nature of the traffic characteristics, ever-changing network environment and its market demands. One of the main problems that we address in this paper is that how much the firewall rules are useful, up-to-dated, well-organized or efficient to reflect the current characteristics of network traffics. In this paper, we present a set of techniques and algorithms to analysis and manage firewall policy rules: (1) Data Mining technique to deduce efficient firewall policy rules by mining its network traffic log based on its frequency, (2) Filtering-Rule Generalization (FRG) to reduce the number of policy rules by generalization, and (3) a technique to identify any decaying rule and a set of few dominant rules, to generate a new set of efficient firewall policy rules. The anomaly detection based on the mining exposes many hidden but not detectable by analyzing only the firewall policy rules, resulting in two new types of the anomalies. As a result of these mechanisms, network security administrators can automatically review and update the rules. We have developed a prototype system and demonstrated usefulness of our approaches.
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paper_title: Data Clustering for Anomaly Detection in Network Intrusion Detection

paper_content:

Intrusions pose a serious security risk in a network environment. Network intrusion detection systems aim to identify attacks or malicious activity in a network with a high detection rate while maintaining a low false alarm rate. New emerging threats or attacks are the most di cult to detect. Signature based methods and misuse detection methods, which rely on labeled patterns, can detect previously known attacks with good accuracy but are unable to detect new types of attacks. In addition, maintaining the signature data base and labeling the patterns is time consuming and expensive. Anomaly detection techniques can make use of unsupervised learning methods to identify new emerging threats with no need of labeled patterns, but, with a potential false alarm rate. We reviewed the di erent network intrusion detection methods and present here a comparative study with more emphasis on the unsupervised learning methods for anomaly detection. The Kmeans algorithm was chosen to evaluate the performance of an unsupervised learning method for anomaly detection using the Kdd Cup 1999 network data set. The results of the evaluation con rm that a high detection rate can be achieve while maintaining a low false alarm rate.
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paper_title: The Data-Mining Industry Coming of Age

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The information revolution is generating mountains of data, from sources as diverse as credit card transactions, telephone calls, Web clickstreams, space science, and human genome research. At the same time, faster and cheaper storage technology lets us store greater amounts of data online, and better database management system software provides easy access to those databases. The paper discusses data mining applications and trends.
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paper_title: The cost of a cloud: research problems in data center networks

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The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. Accordingly, we first examine the costs of cloud service data centers today. The cost breakdown reveals the importance of optimizing work completed per dollar invested. Unfortunately, the resources inside the data centers often operate at low utilization due to resource stranding and fragmentation. To attack this first problem, we propose (1) increasing network agility, and (2) providing appropriate incentives to shape resource consumption. Second, we note that cloud service providers are building out geo-distributed networks of data centers. Geo-diversity lowers latency to users and increases reliability in the presence of an outage taking out an entire site. However, without appropriate design and management, these geo-diverse data center networks can raise the cost of providing service. Moreover, leveraging geo-diversity requires services be designed to benefit from it. To attack this problem, we propose (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state.
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paper_title: DATA MINING TRENDS AND DEVELOPMENTS : The Key Data Mining Technologies and Applications for the 21 st Century

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This paper discusses a number of technologies, approaches, and research areas which have been identified as having critical and future promise in the field of data mining. There is currently an explosion in the amount of data which we now produce and have access to, and mining from these sources can uncover important information. The extensive use of handheld, wireless, and other ubiquitous devices is a developing area, since a lot of information being created and transmitted would be maintained and stored only on these kinds of devices. Among the other areas which are being developed, investigated, and applications identified for include hypertext and hypermedia data mining, phenomenal data mining, distributed/collective data mining, constraint-based data mining, and other related methods.
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paper_title: Addressing cloud computing security issues

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The recent emergence of cloud computing has drastically altered everyone's perception of infrastructure architectures, software delivery and development models. Projecting as an evolutionary step, following the transition from mainframe computers to client/server deployment models, cloud computing encompasses elements from grid computing, utility computing and autonomic computing, into an innovative deployment architecture. This rapid transition towards the clouds, has fuelled concerns on a critical issue for the success of information systems, communication and information security. From a security perspective, a number of unchartered risks and challenges have been introduced from this relocation to the clouds, deteriorating much of the effectiveness of traditional protection mechanisms. As a result the aim of this paper is twofold; firstly to evaluate cloud security by identifying unique security requirements and secondly to attempt to present a viable solution that eliminates these potential threats. This paper proposes introducing a Trusted Third Party, tasked with assuring specific security characteristics within a cloud environment. The proposed solution calls upon cryptography, specifically Public Key Infrastructure operating in concert with SSO and LDAP, to ensure the authentication, integrity and confidentiality of involved data and communications. The solution, presents a horizontal level of service, available to all implicated entities, that realizes a security mesh, within which essential trust is maintained.
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paper_title: Security of cloud computing, storage, and networking

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Convergence and ubiquity are the key characteristics of tomorrows service provision infrastructures. Cloud architectures will constitute cost-efficient backbones that will support the transmission, storage, and computing of the applications contents. These architectures can be used for business, scientific, and pervasive computing purposes. The diversity of the services delivered through cloud infrastructures increases their vulnerability to security incidents and attacks. The cost and complexity reduction requirements render the design and development of protection mechanisms even more challenging. In addition, key design features such as confidentiality, privacy, authentication, anonymity, survivability, dependability, and faulttolerance are, in some extent, conflicting. The objective of this tutorial is to present the state-of-the-art of security and explore research directions and technology trends to address the protection of cloud communications and networking infrastructures. An emphasis will be made on the collaboration of mobile devices in cloud based infrastructures. The fundamental concepts of cloud computing security will be explored, including cloud security services, cloud security principles, cloud security requirements, and testing techniques.
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paper_title: Towards secure mobile cloud computing: A survey

paper_content:

Mobile cloud computing is gaining popularity among mobile users. The ABI Research predicts that the number of mobile cloud computing subscribers is expected to grow from 42.8 million (1.1% of total mobile users) in 2008 to 998 million (19% of total mobile users) in 2014. Despite the hype achieved by mobile cloud computing, the growth of mobile cloud computing subscribers is still below expectations. According to the recent survey conducted by the International Data Corporation, most IT Executives and CEOs are not interested in adopting such services due to the risks associated with security and privacy. The security threats have become a hurdle in the rapid adaptability of the mobile cloud computing paradigm. Significant efforts have been devoted in research organizations and academia to build secure mobile cloud computing environments and infrastructures. In spite of the efforts, there are a number of loopholes and challenges that still exist in the security policies of mobile cloud computing. This literature review: (a) highlights the current state of the art work proposed to secure mobile cloud computing infrastructures, (b) identifies the potential problems, and (c) provides a taxonomy of the state of the art.
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paper_title: A novel approach for security in Cloud Computing using Hidden Markov Model and clustering

paper_content:

In today's fastest growing IT industry Cloud Computing is gaining much more popularity because the Cloud providers feel that it is very easy to manage the data in the cloud environment rather than normal web-sites in form of simple web pages. Every day the data seeking is being done by many users immensely. Here due to immense number of users seeking the data on daily basis there is a serious security concerns to the cloud providers as well as the data providers who put their data on the cloud computing environment. This paper provides solution of the security issues of cloud computing with the help of Hidden Markov Model based Clustering using data mining Techniques which can be very fruitful technique to detect any kind of intrusion detection in the network.
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paper_title: A novel approach for security in Cloud Computing using Hidden Markov Model and clustering

paper_content:

In today's fastest growing IT industry Cloud Computing is gaining much more popularity because the Cloud providers feel that it is very easy to manage the data in the cloud environment rather than normal web-sites in form of simple web pages. Every day the data seeking is being done by many users immensely. Here due to immense number of users seeking the data on daily basis there is a serious security concerns to the cloud providers as well as the data providers who put their data on the cloud computing environment. This paper provides solution of the security issues of cloud computing with the help of Hidden Markov Model based Clustering using data mining Techniques which can be very fruitful technique to detect any kind of intrusion detection in the network.
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Title: Application of Data Mining Techniques for Information Security in a Cloud: A Survey

Section 1: INTRODUCTION
Description 1: Introduce the significance of cloud security, and outline the scope and structure of the survey paper.

Section 2: LITERATURE SURVEY
Description 2: Review existing literature on data mining techniques for information security, highlighting previous work, methods, and applications.

Section 3: INFORMATION SECURITY
Description 3: Explain the fundamental concepts of information security, including key attributes, types of attacks, and associated risks.

Section 4: CLOUD ARCHITECTURE
Description 4: Describe the essentials of cloud computing, including its architecture, service models, deployment models, and inherent security challenges.

Section 5: DATA MINING
Description 5: Detail the basic concepts and techniques of data mining, including clustering, classification, regression, and association rule learning.

Section 6: ROLE OF DATA MINING IN INFORMATION SECURITY
Description 6: Discuss how data mining techniques can be applied to enhance information security, focusing on detection and prevention of intrusions, fraud detection, and other security breaches.

Section 7: CONCLUSION & OPEN ISSUES
Description 7: Summarize the findings of the survey and present open issues and future directions for applying data mining techniques to ensure information security in the cloud.