{"title":"Apoptosis Inspired Intrusion Detection System","authors":"R. Sridevi, G. Jagajothi","volume":94,"journal":"International Journal of Computer and Information Engineering","pagesStart":1890,"pagesEnd":1897,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10000033","abstract":"
Artificial Immune Systems (AIS), inspired by the
\r\nhuman immune system, are algorithms and mechanisms which are
\r\nself-adaptive and self-learning classifiers capable of recognizing and
\r\nclassifying by learning, long-term memory and association. Unlike
\r\nother human system inspired techniques like genetic algorithms and
\r\nneural networks, AIS includes a range of algorithms modeling on
\r\ndifferent immune mechanism of the body. In this paper, a mechanism
\r\nof a human immune system based on apoptosis is adopted to build an
\r\nIntrusion Detection System (IDS) to protect computer networks.
\r\nFeatures are selected from network traffic using Fisher Score. Based
\r\non the selected features, the record\/connection is classified as either
\r\nan attack or normal traffic by the proposed methodology. Simulation
\r\nresults demonstrates that the proposed AIS based on apoptosis
\r\nperforms better than existing AIS for intrusion detection.<\/p>\r\n","references":"[1] Hui Wang, Guoping Zhang, Huiguochen and Xueshu Jiang, \u201cMining\r\nAssociation Rules for Intrusion Detection\u201d,2009 IEEE International\r\nconference on frontier of Computer Science and Technology.\r\n[2] ChristophEhret, Ulrich Ultes-Nitsche, Immune System Based Intrusion\r\nDetection System University of Fribourg Department of Computer\r\nScience, University of Fribourg,Boulevard de P\u00e9rolles 90, CH-1700\r\nFribourg, Switzerland.\r\n[3] S. Northcutt and J. Novak, \u201cNetwork Intrusion Detection:An Analyst\u2019s\r\nHandbook,\u201d 2nd Edition, New Riders Publishing,Berkeley, 2000.\r\n[4] Karen Scarfone, Peter Mell, Guide to intrusion detection and prevention\r\nsystems (IDPS) Special Publication 800-.94,2007\r\n[5] L de Castro, J Timmis, Artificial Immune Systems: A New\r\nComputational Intelligence Approach, Springer Verlag, 2002.\r\n[6] Sophia Kaplantzis, Nallasamy Mani, A Study on Classification\r\nTechniques for Network Intrusion Detection\r\n[7] U. Aickelin and D. Dasgupta, Artificial Immune Systems Search\r\nMethodologies: Introductory Tutorials in Optimization and Decision\r\nSupport Techniques,2008.\r\n[8] DipankarDasgupta, Artificial Immune Systems: A Bibliography CS\r\nTechnical Report No. CS-07-004 December 2007 Version 5.8.\r\n[9] John E. Hunt and Denise E. Cooke, Learning using an artificial immune\r\nsystem, Journal of Network and Computer Applications (1996) 19, 189\u2013\r\n212 \u00d3 1996 Academic Press\r\n[10] ChingthamTejbanta Singh, and Shivashankar B. Nair, An Artificial\r\nImmune System for a MultiAgent Robotics System, World Academy of\r\nScience, Engineering and Technology 11 2005\r\n[11] S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri. Self-nonself\r\ndiscrimination in a computer. Proceedings of the 1994 IEEE Symposium\r\non Research in Security and Privacy, pages 202\u2013212, Oakland, CA,\r\n1994. IEEE Computer Society Press.\r\n[12] Shaik Akbar, Dr. K. Nageswara Rao, Dr. J. A. Chandulal, Intrusion\r\nDetection System Methodologies Based on Data Analysis, International\r\nJournal of Computer Applications (0975 \u2013 8887) Volume 5\u2013 No.2,\r\nAugust 2010\r\n[13] Zhao junzhonghuanghoukuan , An evolving intrusion detection system\r\nbased on natural immune system proceedings of IEEE TENCON\u201902\r\n[14] Leandro N. de Castro and Jon Timmis(2002). An artificial immune\r\nnetwork for multimodal function optimization. In IEEE Congress on\r\nEvolutionary Computation (CEC), pages 699\u2013704.\r\n[15] Gu, Q., & Han, J. (2011, October). Towards feature selection in\r\nnetwork. In Proceedings of the 20th ACM international conference on\r\nInformation and knowledge management (pp. 1175-1184). ACM.\r\n[16] Gu, Q., Li, Z., & Han, J. (2012). Generalized fisher score for feature\r\nselection. arXiv preprint arXiv:1202.3725.\r\n[17] John M. Hall,AN Investigation into Immune-Based Intrusion Detection,\r\nDecember 2003, University of Idaho.\r\n[18] Kaushik Ghosh and Rajagopalan Srinivasan, Immune-System-Inspired\r\nApproach to Process Monitoring and Fault Diagnosis, Copyright \u00a9 2010\r\nAmerican Chemical Society.\r\n[19] De Castro, L. N. &Timmis, J. I. (2002). Artificial Immune Systems: A\r\nNovel Paradigm for Pattern Recognition, In : Artificial Neural Networks\r\nin Pattern Recognition, L. Alonso, J. Corchado, C. Fyfe, 67-84,\r\nUniversity of Paisley.\r\n[20] K. Regina, A. Boukerche, J. Bosco, M. Notare, \u201cHuman Immune\r\nAnomaly and Misuse Based Detection for Computer System Operations:\r\nPart II\u201d, Proceedings of the International Parallel and Distributed\r\nProcessing Symposium 2003, IEEE \u00a9 2003.\r\n[21] Zhu, Dan , Data mining for network intrusion detection: A comparison\r\nof alternative methods Decision Sciences Date: Monday, October 1\r\n2001.\r\n[22] A. Watkins and L. Boggess, \u201cA new classifier based on resource\r\nlimitedartificial immune systems,\u201d in Proc. Congr. Evol. Comput., May\r\n2002,pp. 1546\u20131551.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 94, 2014"}