Computer Immunity Using an Intrusion Detection System (IDS)

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Computers are involved in every aspect of modern society and have become an essential part of our lives, but their vulnerability is of increasing concern to us. Security flaws are inherent in the operation of computers Most flaws are caused by errors in the process of software engineering or unforeseen mishaps and it is difficult to solve these problems by conventional methods. A radical way of constantly monitoring the system for newly disclosed vulnerabilities is required. In order to devise such a system, this work draws an analogy between computer immune systems and the human immune system. The computer immune system is the equivalent of the human immune system. The primary objective of this paper is to use an intrusion detection system in the design and implementation of a computer immune system that would be built on the framework of the human immune system. This objective is successfully realized and in addition a prevention mechanism using the windows IP Firewall feature has been incorporated. Hence the system is able to perform intrusion detection and prevention. Data was collected about events occurring in a computer network that violate predefined security policy, such as attempts to affect the confidentiality, integrity or its availability using Snort rules for known attacks and adaptive detection for the unknown attacks. The system was tested using real-time data and Intrusion Detection evaluation (IDEVAL) Department of Defense Advanced Research Projects Agency (DARPA) data set. The results were quite encouraging as few false positive were recorded.

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200-205

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Hofmeyr S. A. and Forrest S. (2000): Architecture for an Artificial Immune System, Evolutionary Computation, vol. 7, No. 1, pp.45-68.

Google Scholar

[2] Bace R. and Mell P. (2001): Intrusion Detection Systems. Technical Report NIST Computer Science Special Reports SP-800-31, National Institute of Standards and Technology, November (2001).

Google Scholar

[3] Jackson, K. (1999): Intrusion detection system product survey. Research report LA-UR-99-3883, Los Alamos National Laboratory.

Google Scholar

[4] Yu-Xin Ding, Min Xiao, Ai-Wu Liu (2009): Research and Implementation on Snort-based hybrid Intrusion detection system, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July (2009).

DOI: 10.1109/icmlc.2009.5212282

Google Scholar

[5] Middlemiss M. (2005): Framework for Intrusion Detection Inspired by the Immune System. The Information Science Discussion Paper Series Number 2005/07, July 2005 ISSN 1172-6024.

Google Scholar

[6] Roesch Martin and Green Chris (2012): Snort Users Manual 2. 9. 2. Sourcefire, Inc.

Google Scholar

[7] Donald E. Knuth (1998): The Art of Computer Programming, volume 2: Seminumerical Algorithms, 3rd edn., p.232. Boston: Addison-Wesley.

Google Scholar

[8] De Castro, L.N., Timmis, J. (2002): Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag.

Google Scholar

[9] Chunfu Jia and Deqiang Chen (2009): Performance Evaluation of a Collaborative Intrusion Detection. College of Information Technology and Science, Nankai University, Tianjin 300071, China cfjia@nankai. edu. cn.

Google Scholar