Intrusion Detection System Using Artificial Immune System and New Multi Core Technology


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Intrusion detection systems (IDS) are nowadays very important for every IT company which is concerned with security and sensitive systems. A perfect IDS has still not been found and it stays a hot and challenging area in computer security research. Recently a new approach started to make its way to intrusion detection, namely the immune system. It has a lot of interesting features we would like to find in IDS. A new artificial intelligence paradigm was new compared to neural networks or fuzzy logic, but it is very promising for different areas in computer science. By abstractly comparing the way an intrusion detection system and the human immune system work, it is noticed some similarities. Within this context it is normal to use as much similarities as possible to improve IDS and to see how to implement the different features; this is where the artificial immune systems paradigm will help. This paper employs a modification method on the artificial immune system which known as LISYS (Light weight Immune System) through the using of new technology of Multi Core processor, the modification was running an individual instance of LISYS on each core of the processor which increase the number of detectors used by each node that used as an AIS node in the network, the experimental results shows that the detection efficiency was well improved.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


Immune System; Negative Selection; Clonal Selection Algorithm; Network Intrusion Detection

Full Text:

PDF


References


Mykerjee, B., Heberlein, L. T. and Levitt, K. N., Network Intrusion Detection. IEEE Network, 1994. 8(3): p. 26-41.

Kephart, J. A biologically inspired immune system for computers.in Proceedings of the Fourth International Workshopon Synthesis and Simulation of Living Systems, Artificial Life IV.1994.

J. O. Kephart, G.B.S., W. C. Arnold, D. M. Chess, G. J.Teasuro, and S. R.Biologically Inspired Defences against Computer Viruses. Machie Learning and Data Mining: Methodand Applications, p. 313-334.1997.

J. O. Kephart, G.B.S., M. Swimmer, and S. R., Blueprint for a Computer Immune System. Artificial Immune Systems and TheirApplications, p. 241-261, 1998.

Hofmeyr S, F.S., Architecture for an artificial immune system.Evol Computer, 2000.

Hofmeyr, S.A., An Immunological Model of Distributed Detection and its Application to Network Security. PhD thesis,University of New Mexico, 1999.

J. Kim, P.B., The artificial immune model for network intrusion detection. 7th European Conf. on Intelligent Techniques and Soft Computing. Aachen, Germany, 1999.

J. Kim, B.P.J., Towards an artificial immune system fornetwork intrusion detection: an investigation of clonal selection with a negative selection operator the Congress on Evolutionary Computation, Seoul, Korea, p. 1244-1252, 2001.

Kim, J.a.B., P. J., Towards an Artificial Immune System for Network Intrusion Detection: An Investigation of Dynamic Clonal Selection. Proceedings of Congress on Evolutionary Computation, p. 1015-1020, 2002.

M. Ayara, J.T., R. de Lemos, L. N. de Castro, and R. Duncan.Negative selection: How to generate detectors. in Proceedings ofthe 1st International Conference on Artificial Immune Systems(ICARIS'-02). September 2002.

Matzinger, P., Tolerance, danger, and the extended family.Annual Review of Immunology, Vol 12, p 991-1045, 1994.

Burgess, M. Computer immunology. in Proc. of the Systems Administration Conference (LISA-98), 1998.

Shahabi C, Zarkesh A M, Adibi J, et al. Natural immune system:IEEE Press, 2001

Bezdek J C, Hathaway R, Sabin M and Tucker W. Bind reaction. Man. Cybernet,vol 17 (5) : 873 877, 1987.

Balbir Singh and Cyrus Daneshvar Clin. Microbiol. Rev. 26:165-184April 2013

L. N. de Castro and J. Timmis. “Artificial Immune Systems: A New Computational Intelligence Approach”, Springer, 2002.Press,2001.

J.D. Farmer, N. Packard and A. Perelson. "The immune system,adaptation and machine learning", in Physica D, vol. 2, pp. 187-204,1986.

S. Forrest, A. Perelson, L. Allen and R. Cherukuri. “Self-Nonself Discrimination in a Computer”, in Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, 1994.

Revisiting LISYS: Parameters and Normal Behavior Justin Balthrop, Stephanie Forrest, and Matthew R. Glickman Computer Science Department University of New Mexico Albuquerque,NM 87131.

B. Mukherjee, L. T. Heberlein, and K. N. Levitt. Network intrusion detection.IEEE Network,pages 26-41, 1994.

L. Heberlein, G. Dim, K. Levitte, B. Mukherjee ,J. Wood, and D. Wolber. A network security monitor. In Proc. Of the IEEESymp. On Security and Privacy .IEEPress,1990.

S. Hofmeyr. An immunological model of distributed detection andits application tocomputer security.PhD ths.,UNM,1999.

J. Percus, 0. Percus, and A. Perelson. Predicting the size of the antibody combining region from consideration of efficientself/non-self discrimination.Proc.of the National Academy of Science, 90:1691 1695, 1993.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize