Intrusion Detection System using Artificial Immune Systems: A Case Study

. Ojasvini, . Nitesh, . Piyush, Narina Thakur, Arvind Rehalia


Networks are working at their apical efficiency and are increasing in size by every second; emergence of various threats becomes hindrance in the growth and privacy of the users. The network is vulnerable to security breaches, due to malicious nodes. Intrusion detection systems aim at removing this vulnerability. In this paper, intrusion detection mechanisms for large-scale dynamic networks are investigated. Artificial immune system is a concept that works to protect a network the way immune systems of vertebrates work in nature. This paper also illustrates this artificial immune system, the integration of bio-inspired algorithms, and its functionality with the computer networks.

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Hofmeyr, Steven A., and Stephanie Forrest. "Architecture for an artificial immune system." Architecture 8.4 (2006) .

Deaton, R., et al. "A DNA based artificial immune system for self-nonself discrimination." Systems, man, and Cybernetics, 1997. Computational Cybernetics and simulation., 1997 IEEE International Conference on. Vol. 1. IEEE, 1997.

P. Matzinger. Tolerance, danger and the extended family. Annual Review of Immunology, 12:991–1045, 1994.

P. Matzinger. An innate sense of danger. Seminars in Immunology, 10:399–415, 1998.

Dasgupta, Dipankar, Zhou Ji, and Fabio Gonzalez. "Artificial immune system (AIS) research in the last five years." Evolutionary Computation, 2003. CEC'03. The 2003 Congress on. Vol. 1. IEEE, 2003.

Ayara, Modupe, et al. "Negative selection: How to generate detectors." Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS). Vol. 1. Canterbury, UK:[sn], 2002.

Forrest, Stephanie, et al. "Self-nonself discrimination in a computer." Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on. Ieee, 1994.

Taylor, Dan, and David Corne. "An investigation of the negative selection algorithm for fault detection in refrigeration systems." Artificial Immune Systems (2003): 34-45.

J. K. Percus, O. E. Percus, and A. S. Perelson. Predicting the size of the antibody-combining region from consideration of efficient self/non-self discrimination. In Proceedings of the National Academy of Science 90, pages 1691–1695, 1993.

Navarro, Gonzalo. "A guided tour to approximate string matching." ACM computing surveys (CSUR) 33.1 (2001): 31-88.



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