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Reseach Article

Artificial Immune System based Intrusion Detection with Fisher Score Feature Selection

Published on January 2013 by R. Sridevi, G. Jagajothi, Rajan Chattemvelli
Amrita International Conference of Women in Computing - 2013
Foundation of Computer Science USA
AICWIC - Number 3
January 2013
Authors: R. Sridevi, G. Jagajothi, Rajan Chattemvelli
2c0d9da6-7063-4084-a232-b50dc490ec88

R. Sridevi, G. Jagajothi, Rajan Chattemvelli . Artificial Immune System based Intrusion Detection with Fisher Score Feature Selection. Amrita International Conference of Women in Computing - 2013. AICWIC, 3 (January 2013), 7-11.

@article{
author = { R. Sridevi, G. Jagajothi, Rajan Chattemvelli },
title = { Artificial Immune System based Intrusion Detection with Fisher Score Feature Selection },
journal = { Amrita International Conference of Women in Computing - 2013 },
issue_date = { January 2013 },
volume = { AICWIC },
number = { 3 },
month = { January },
year = { 2013 },
issn = 0975-8887,
pages = { 7-11 },
numpages = 5,
url = { /proceedings/aicwic/number3/9874-1316/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Amrita International Conference of Women in Computing - 2013
%A R. Sridevi
%A G. Jagajothi
%A Rajan Chattemvelli
%T Artificial Immune System based Intrusion Detection with Fisher Score Feature Selection
%J Amrita International Conference of Women in Computing - 2013
%@ 0975-8887
%V AICWIC
%N 3
%P 7-11
%D 2013
%I International Journal of Computer Applications
Abstract

Intrusion-detection systems (IDS) which were essential in computer security because of difficulties in ensuring the information systems are security free. Literature has numerous intrusion detection approaches for network security. IDS efficiency was based on the ability to differentiate between normal and harmful activity. Hence, it becomes crucial to achieve better detection rates and lower false alarm rates in IDS. Automated/adaptive detection systems should secure the system handling present and possible threats in the future. Features extracted from network traffic by the IDS, classify the record/connection as either an attack or normal traffic. So, feature selection has a major role in IDS performance. This paper adopts a feature selection using the Fisher Score. Artificial Immune Systems (AIS) based IDS to detect and defend against harmful, unknown invaders is proposed. Evaluation of security detection mechanisms is done through the KDD-cup dataset.

References
  1. R. Bace and P. Mell. "Intrusion Detection Systems", NIST Special Publication 800-31. 2001.
  2. S. Northcutt & J. Novak, "Network Intrusion Detection: An Analyst's Handbook," 2nd Edition, New Riders Publishing, Berkeley, 2000.
  3. Debar, H. (2002). An Introduction to Intrusion-Detection Systems. In Proceedings of Connect'2000.
  4. Vera Marinova-Boncheva, 2007, "A Short Survey of Intrusion Detection Systems", Problems Of Engineering Cybernetics And Robotics, pp. 23 – 30.
  5. Kim J, Bentley P (1999), The Artificial Immune Model for Network Intrusion Detection, 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT'99).
  6. R. Sridevi, G. Jagajothi and RajanChattemvelli, A PCA-AIS Approach for Intrusion Detection, International Journal of Computer Science and Telecommunications, Volume 3, Issue 7, July 2012], pp:104-108.
  7. U Aickelin, P Bentley, S Cayzer, J Kim, J McLeod, Danger Theory: The Link between AIS and IDS?, Proceedings ICARIS-2003, 2nd International Conference on Artificial Immune Systems, pp 147-155, 2003.
  8. Simon T. Powers, A Hybrid Artificial Immune System and Self Organising Map for Network Intrusion Detection, Information Sciences 178(15), pp. 3024-3042, August 2008.
  9. Divyata Dal, Siby Abraham, Ajith Abraham,and MukundSanglikar, Evolution Induced Secondary Immunity: An Artificial Immune System based Intrusion Detection System, in: the 2008 7th Computer Information systems and Industrial Management Applications (IEEE Computer Society Washington, DC, USA, 2008) 65-70.
  10. 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.
  11. Kuby J (2002), Immunology, Fifth Edition WH Freeman by RA Goldsby, TJ Kindt, BA Osborne.
  12. Tarakanov, A. O. , Skormin, V. A. , Sokolova, S. P. , & Sokolova, S. S. (2003). Immunocomputing: principles and applications. Springer-Verlag.
  13. Aickelin, U. , Greensmith, J. and Twycross, J. , 2004, Immune system approaches to intrusion detection—a review, in: Proc. ICARIS-04, 3rd Int. Conf. on Artificial Immune Systems (Catania, Italy), pp. 316–329, Springer, Berlin. Amazon, 2003
  14. Simon M. Garrett, "How Do We Evaluate Artificial Immune Systems?", Evolutionary Computation 13(2):, 2005, Massachusetts Institute of Technology, pp. 145-178.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (ids) Kdd Cup 99 Dataset Fisher Score For Feature Selection Artificial Immune Systems (ais)