A Review of Machine Learning based Anomaly Detection Techniques
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paper_title: Guide to Intrusion Detection and Prevention Systems (IDPS

paper_content:

This is a Hard copy of the NIST Special Publication 800-94 Guide to Intrusion Detection and Prevention Systems (IDPS). This publication describes the characteristics of IDPS technologies and provides recommendations for designing, implementing, configuring, securing, monitoring, and maintaining them. The types of IDPS technologies are differentiated primarily by the types of events that they monitor and the ways in which they are deployed. This publication discusses the following four types of IDPS technologies: Network-Based, which monitors network traffic for particular network segments or devices and analyzes the network and application protocol activity to identify suspicious activity Wireless, which monitors wireless network traffic and analyzes it to identify suspicious activity involving the wireless networking protocols themselves Network Behavior Analysis (NBA), which examines network traffic to identify threats that generate unusual traffic flows, such as distributed denial of service (DDoS) attacks, certain forms of malware, and policy violations (e.g., a client system providing network services to other systems) Host-Based, which monitors the characteristics of a single host and the events occurring within that host for suspicious activity. Implementing the following recommendations should facilitate more efficient and effective intrusion detection and prevention system use for Federal departments and agencies. Disclaimer This hardcopy is not published by National Institute of Standards and Technology (NIST), the US Government or US Department of Commerce. The publication of this document should not in any way imply any relationship or affiliation to the above named organizations and Government.
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paper_title: Guide to Intrusion Detection and Prevention Systems (IDPS

paper_content:

This is a Hard copy of the NIST Special Publication 800-94 Guide to Intrusion Detection and Prevention Systems (IDPS). This publication describes the characteristics of IDPS technologies and provides recommendations for designing, implementing, configuring, securing, monitoring, and maintaining them. The types of IDPS technologies are differentiated primarily by the types of events that they monitor and the ways in which they are deployed. This publication discusses the following four types of IDPS technologies: Network-Based, which monitors network traffic for particular network segments or devices and analyzes the network and application protocol activity to identify suspicious activity Wireless, which monitors wireless network traffic and analyzes it to identify suspicious activity involving the wireless networking protocols themselves Network Behavior Analysis (NBA), which examines network traffic to identify threats that generate unusual traffic flows, such as distributed denial of service (DDoS) attacks, certain forms of malware, and policy violations (e.g., a client system providing network services to other systems) Host-Based, which monitors the characteristics of a single host and the events occurring within that host for suspicious activity. Implementing the following recommendations should facilitate more efficient and effective intrusion detection and prevention system use for Federal departments and agencies. Disclaimer This hardcopy is not published by National Institute of Standards and Technology (NIST), the US Government or US Department of Commerce. The publication of this document should not in any way imply any relationship or affiliation to the above named organizations and Government.
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paper_title: FuGeIDS: Fuzzy Genetic paradigms in Intrusion Detection Systems

paper_content:

With the increase in the number of security threats, Intrusion Detection Systems have evolved as a significant countermeasure against these threats. And as such, the topic of Intrusion Detection Systems has become one of the most prominent research topics in recent years. This paper gives an overview of the Intrusion Detection System and looks at two major machine learning paradigms used in Intrusion Detection System, Genetic Algorithms and Fuzzy Logic and how to apply them for intrusion detection.
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paper_title: Anomaly-based network intrusion detection: Techniques, systems and challenges

paper_content:

The Internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continually, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, anomaly-based network intrusion detection techniques are a valuable technology to protect target systems and networks against malicious activities. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. This paper begins with a review of the most well-known anomaly-based intrusion detection techniques. Then, available platforms, systems under development and research projects in the area are presented. Finally, we outline the main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues.
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Title: A Review of Machine Learning based Anomaly Detection Techniques

Section 1: INTRODUCTION
Description 1: Introduce the concept of intrusion detection systems (IDS), their importance, and the main types of detection techniques.

Section 2: DETECTION TECHNIQUES
Description 2: Discuss and differentiate between signature-based detection and anomaly-based detection techniques.

Section 3: MACHINE LEARNING BASED TECHNIQUES
Description 3: Provide an overview of the various machine learning techniques used for anomaly detection, including subcategories.

Section 4: Fuzzy Logic
Description 4: Explain the application of fuzzy logic in anomaly detection, including its rationale and effectiveness.

Section 5: Genetic Algorithms
Description 5: Describe how genetic algorithms are used in anomaly detection, including their methodologies and advantages.

Section 6: Neural Networks
Description 6: Discuss neural networks as a technique for anomaly detection, highlighting their generalization capability and potential.

Section 7: Bayesian Networks
Description 7: Detail the use of Bayesian networks in anomaly detection, emphasizing their probabilistic modeling and benefits.

Section 8: PROS/CONS OF ANOMALY DETECTION
Description 8: Evaluate the benefits and drawbacks of anomaly-based detection systems, focusing on their ability to detect unknown misuse and the challenge of false alarms.

Section 9: CONCLUSION
Description 9: Summarize the paper by revisiting the main points discussed, particularly the different anomaly detection techniques and their significance in IDS.