Apoptosis Inspired Intrusion Detection System

Authors: R. Sridevi, G. Jagajothi

Abstract:

Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.

Keywords: Apoptosis, Artificial Immune System (AIS), Fisher Score, KDD dataset, Network intrusion detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097467

References:

[1] Hui Wang, Guoping Zhang, Huiguochen and Xueshu Jiang, “Mining Association Rules for Intrusion Detection”,2009 IEEE International conference on frontier of Computer Science and Technology.
[2] ChristophEhret, Ulrich Ultes-Nitsche, Immune System Based Intrusion Detection System University of Fribourg Department of Computer Science, University of Fribourg,Boulevard de Pérolles 90, CH-1700 Fribourg, Switzerland.
[3] S. Northcutt and J. Novak, “Network Intrusion Detection:An Analyst’s Handbook,” 2nd Edition, New Riders Publishing,Berkeley, 2000.
[4] Karen Scarfone, Peter Mell, Guide to intrusion detection and prevention systems (IDPS) Special Publication 800-.94,2007
[5] L de Castro, J Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer Verlag, 2002.
[6] Sophia Kaplantzis, Nallasamy Mani, A Study on Classification Techniques for Network Intrusion Detection
[7] U. Aickelin and D. Dasgupta, Artificial Immune Systems Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques,2008.
[8] DipankarDasgupta, Artificial Immune Systems: A Bibliography CS Technical Report No. CS-07-004 December 2007 Version 5.8.
[9] John E. Hunt and Denise E. Cooke, Learning using an artificial immune system, Journal of Network and Computer Applications (1996) 19, 189– 212 Ó 1996 Academic Press
[10] ChingthamTejbanta Singh, and Shivashankar B. Nair, An Artificial Immune System for a MultiAgent Robotics System, World Academy of Science, Engineering and Technology 11 2005
[11] S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri. Self-nonself discrimination in a computer. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, pages 202–212, Oakland, CA, 1994. IEEE Computer Society Press.
[12] Shaik Akbar, Dr. K. Nageswara Rao, Dr. J. A. Chandulal, Intrusion Detection System Methodologies Based on Data Analysis, International Journal of Computer Applications (0975 – 8887) Volume 5– No.2, August 2010
[13] Zhao junzhonghuanghoukuan , An evolving intrusion detection system based on natural immune system proceedings of IEEE TENCON’02
[14] Leandro N. de Castro and Jon Timmis(2002). An artificial immune network for multimodal function optimization. In IEEE Congress on Evolutionary Computation (CEC), pages 699–704.
[15] Gu, Q., & Han, J. (2011, October). Towards feature selection in network. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1175-1184). ACM.
[16] Gu, Q., Li, Z., & Han, J. (2012). Generalized fisher score for feature selection. arXiv preprint arXiv:1202.3725.
[17] John M. Hall,AN Investigation into Immune-Based Intrusion Detection, December 2003, University of Idaho.
[18] Kaushik Ghosh and Rajagopalan Srinivasan, Immune-System-Inspired Approach to Process Monitoring and Fault Diagnosis, Copyright © 2010 American Chemical Society.
[19] De Castro, L. N. &Timmis, J. I. (2002). Artificial Immune Systems: A Novel Paradigm for Pattern Recognition, In : Artificial Neural Networks in Pattern Recognition, L. Alonso, J. Corchado, C. Fyfe, 67-84, University of Paisley.
[20] K. Regina, A. Boukerche, J. Bosco, M. Notare, “Human Immune Anomaly and Misuse Based Detection for Computer System Operations: Part II”, Proceedings of the International Parallel and Distributed Processing Symposium 2003, IEEE © 2003.
[21] Zhu, Dan , Data mining for network intrusion detection: A comparison of alternative methods Decision Sciences Date: Monday, October 1 2001.
[22] A. Watkins and L. Boggess, “A new classifier based on resource limitedartificial immune systems,” in Proc. Congr. Evol. Comput., May 2002,pp. 1546–1551.