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Reseach Article

A Study on Swarm Intelligence Techniques in Intrusion Detection

Published on November 2012 by P. Amudha, H. Abdul Rauf
Computational Intelligence & Information Security
Foundation of Computer Science USA
CIIS - Number 1
November 2012
Authors: P. Amudha, H. Abdul Rauf
9884d057-04a7-4f48-9f76-70210c60c244

P. Amudha, H. Abdul Rauf . A Study on Swarm Intelligence Techniques in Intrusion Detection. Computational Intelligence & Information Security. CIIS, 1 (November 2012), 9-16.

@article{
author = { P. Amudha, H. Abdul Rauf },
title = { A Study on Swarm Intelligence Techniques in Intrusion Detection },
journal = { Computational Intelligence & Information Security },
issue_date = { November 2012 },
volume = { CIIS },
number = { 1 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 9-16 },
numpages = 8,
url = { /specialissues/ciis/number1/9412-1002/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Computational Intelligence & Information Security
%A P. Amudha
%A H. Abdul Rauf
%T A Study on Swarm Intelligence Techniques in Intrusion Detection
%J Computational Intelligence & Information Security
%@ 0975-8887
%V CIIS
%N 1
%P 9-16
%D 2012
%I International Journal of Computer Applications
Abstract

Intrusion Detection System is a security support mechanism which has received great attention from researchers all over the globe recently. In the recent past, bio-inspired meta-heuristic technique such as swarm intelligence is being proposed for intrusion detection. Swarm Intelligence approaches are used to solve complicated problems by multiple simple agents without centralized control. The swarm intelligence algorithms inspired by animal behaviour in nature such as ants finding shortest path in finding food; a flock of birds flies or a school of fish swims in unison, changing directions in an instant without colliding with each other has been successfully applied to optimization, robotics and military applications. But however, its application to the intrusion detection domain is limited but interesting and inspiring. This paper provides an overview of the research progress in swarm intelligence techniques to the problem of intrusion detection.

References
  1. Abadeh MS, Habibi J. 2010. A hybridization of evolutionary fuzzy systems and ant colony optimization for intrusion detection. The ISC International Journal of Information Security, vol. 2, no. 1, 33-46.
  2. Abadeh MS, Habibi J, Soroush E. 2008. Induction of fuzzy classi?cation systems via evolutionary ACO-based Algorithms. International Journal of Simulation, Systems, Science, Technology, vol. 9, no. 3.
  3. Alipour H, Khosrowshahi E, Esmaeili M, Nourhossein M. 2008. ACOFCR: applying ACO-based algorithms to induct FCR. In Proceedings of the World Congress on Engineering (IWCE), 12-17.
  4. Arif Jamal Malik, Waseem Shahzad, Farrukh Aslam Khan. 2011. Binary PSO and Random Forests Algorithms for PROBE attacks Detection in a network. In Proceedings of IEEE Congress on Evolutionary Computation, 662-668.
  5. Balajinath B. , Raghavan S. V. 2001. Intrusion Detection through Learning Behaviour Model. International Journal of Computer Communications, vol. 24,1202–1212
  6. Banks A, Vincent J, Anyakoha C. 2008. A review of Particle Swarm Optimization, Part II: Hybridisation, Combinatorial, Multi-criteria and Constrained Optimization, and Indicative Applications, Natural Computing, vol. 7, 109–124.
  7. Banks A,Vincent J, Anyakoha C. 2008. A review of Particle Swarm Optimization, Part I: Background and Development, Natural Computing, vol. 6, 467–484.
  8. Beni, G. , Wang, J. 1989. Swarm Intelligence in Cellular Robotic Systems. In Proceedings of NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy.
  9. Bing Shuang, Jiapin Chen, Zhenbo Li. 2011. Study on Hybrid PS-ACO algorithm, Applied Intelligence, Springer, vol. 34, 64-73.
  10. Chang-Lung T, Chun-Chi T, Chin-Chuan H. 2009. Intrusive behavior analysis based on honey pot tracking and ant algorithm analysis, In Proceedings of the 43rd Annual International Carnahan Conference on Security Technology, 248-252.
  11. Crosbie M. , Spafford E. H. 1995. Applying Genetic Programming to Intrusion Detection, In working Notes for the AAAI Symposium on Genetic Programming, 1-8, MIT.
  12. Deneubourg, Aron, Goss, Pasteels. 1990. The self-organizing exploratory pattern of the Argentine ant, Journal of Insect Behavior, vol. 3, no. 1, 159 - 168.
  13. Dorigo M, Di Caro G. 1999. The ant colony optimization meta-heuristic, New ideas in Optimization, 11-32.
  14. Dorigo M, Di Caro G, Gambardella L. M. 1999. Ant Algorithms for Discrete Optimization, Artificial Life, vol. 5, 137 –172.
  15. Dorigo M, Stutzle T. 2004. Ant colony optimization, MIT Press.
  16. Dréo, J. , Siarry, P. 2002. A new ant colony algorithm using the heterarchical concept aimed at optimization of multiminima continuous functions. Lecture Notes in Computer Science, vol. 1, 216–227.
  17. Goldberg D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co. , Massachusetts.
  18. Hai-HuaGao, Hui-Hua Yang, Xing-Yu Wang. 2005. Ant Colony Optimization Based Network Intrusion Feature Selection and Detection. In Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 3871-3875.
  19. He J, Long D, Chen C. 2007. An Improved Ant-based Classifier for Intrusion Detection. In Proceedings of the Third International Conference on Natural Computation, 819–823.
  20. Holland J. H. 1992. Adaptation in Natural and Artificial Systems: an introductory analysis with applications to biology, control and artificial intelligence. The MIT Press, 2nd edition
  21. Junbing H, Dongyang L, Chuan C. 2007. An improved ant-based classifier for intrusion detection. In Proceedings of the Third International Conference on Natural Computation, 819-823.
  22. KDD99, KDDCup 1999 data. 1999. http://kdd. ics. uci. edu/ Databases/kddcup99/10 percent. gz.
  23. Kennedy J, Eberhart R 1995. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, 1942-1948.
  24. Koza 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.
  25. Koza 1994. Genetic Programming 2: automatic discovery of reusable programs. Complex adaptive systems, MIT Press
  26. Ma J, Liu X, Liu S. 2008a. A new intrusion detection method based on BPSO-SVM. In Proceedings of the International symposium on Computational Intelligence and Design, 473-477.
  27. Michailidis E, Katsikas S K, Georgopoulos E. 2008. Intrusion detection using evolutionary neural networks . In Proceedings of the Panhellenic conference on informatics, 8-12.
  28. Mohamadi H, Habibi J, Saniee Abadeh M. 2008. Misuse intrusion detection using a Fuzzy-Meta-heuristic approach. In Proceedings of 2nd Asia Intl. Conference on modeling and simulation, 439-444.
  29. Muraleedharan R, Osadciw L. A. 2009. An intrusion detection framework for sensor networks using honeypot and Swarm Intelligence. In Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services, 1-2.
  30. Oliveira R. L, De Lima B. S. L. P, Ebecker N. F. F. 2007. A Comparison of bio-inspired meta-heuristic approaches in classification tasks. WIT Transactions on Information and Communication technologies, vol. 38, 25-32.
  31. Parpinelli RS, Lopes HS, Freitas AA. 2002. Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, 21-32
  32. Ramachandran C, Misra S, Obaidat MS. 2008. FORK: A novel two pronged strategy for an agent-based intrusion detection scheme in ad-hoc network. Computer Communications, vol. 31, no. 16, 3855-69.
  33. Socha, Dorigo M. 2006. Ant colony optimization for continuous domains. European Journal of Operational Research, vol. 185, 1155–1173.
  34. Soroush E, Saniee Abadeh M, Habibi JA. 2006. Boosting ant-colony optimization algorithm for computer intrusion detection. In Proceedings of the IEEE 20th International Symposium on Frontiers in Networking with Applications.
  35. Tie-Jun Zhou, Yang Li, Jia Li. 2009. Research on Intrusion Detection of SVM Based on PSO. In Proceedings of Eighth International Conference on Machine Learning and Cybernetics, 1205-1209.
  36. Wang J, Hong X, Ren R, Li T. 2009. A real-time intrusion detection system based on PSO-SVM. In Proceedings of the International Workshop on Information Security and Application, 319-321.
  37. C. Grosan et al. : Swarm Intelligence in Data Mining, Studies in Computational Intelligence (SCI) 34, 1–20 (2006),_Springer-Verlag.
  38. Shelly Xiaonan Wu, Wolfgang Banzhaf. 2010. The use of computational intelligence in intrusion detection systems: A review", Applied Soft Computing, pp. 1-35, Elsevier Publication
  39. Stutzle, T. , & Hoos, H. H. 1996. Improving the Ant System: A detailed report on the MAX-MIN Ant System. Technical report AIDA-96-12, FG Intellektik, FB Informatik, TU Darmstadt, Germany.
  40. Stutzle, T. , & Hoos, H. H. 2000. MAX-MIN Ant System. Future Generation Computer Systems, 16(8), 889–914.
  41. Stutzle, T. 1999. Local Search Algorithms for Combinatorial Problems: Analysis, Improvements, and New Applications, vol. 220 of DISKI. Sankt Augustin, Germany, In?x.
  42. Maniezzo, V. 1999. Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4), 358–369.
  43. Dorigo, M. 1992. Optimization, Learning and Natural Algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano , Milan.
  44. Dorigo, M. , Maniezzo, V. , & Colorni, A. 1991a. Positive feedback as a search strategy. Technical report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milan.
  45. Dorigo, M. , Maniezzo, V. , & Colorni, A. 1991b. The Ant System: An autocatalytic optimizing process. Technical report 91-016 revised, Dipartimento di Elettronica, Politecnico di Milano, Milan.
  46. Dorigo, M. , Maniezzo, V. , & Colorni, A. 1996. Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics—Part B, 26(1), 29–41.
  47. Dorigo, M. , & Gambardella, L. M. 1996. A study of some properties of Ant-Q. In H. Voigt, W. Ebeling, I. Rechenberg, & H. Schwefel (Eds. ), Proceedings of PPSN-IV, Fourth International Conference on Parallel Problem Solving from Nature, vol. 1141 of Lecture Notes in Computer Science (pp. 656–665). Berlin, Springer-Verlag.
  48. Gambardella, L. M. , & Dorigo, M. 1995. Ant-Q: A reinforcement learning approach to the traveling salesman problem. In A. Prieditis & S. Russell (Eds. ), Proceedings of the Twelfth International Conference on Machine Learning (ML-95) (pp. 252–260). Palo Alto, CA, Morgan Kaufmann.
  49. Dorigo, M. , & Gambardella, L. M. 1997a. Ant colonies for the traveling salesman problem. BioSystems, 43(2), 73–81.
  50. Dorigo, M. , & Gambardella, L. M. 1997b. Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.
  51. Bullnheimer, B. , Hartl, R. F. , & Strauss, C. 1997. A new rank based version of the Ant System—A computational study. Technical report, Institute of Management Science, University of Vienna, Austria.
  52. Bullnheimer, B. , Hartl, R. F. , Strauss, C. 1999c. A new rank-based version of the Ant System: computational study. Central European Journal for Operations Research and Economics, 7(1), 25–38.
  53. Blum, C. , & Dorigo, M. 2004. The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics –Part B
  54. Blum, C. , Roli, A. , & Dorigo, M. 2001. HC–ACO: The hyper-cube framework for Ant Colony Optimization. In Proceedings of MIC'2001—Metaheuristics International Conference, vol. 2 (pp. 399–403).
  55. Wang J, Hong X, Ren R, Li T. 2009. A real-time intrusion detection system based on PSO-SVM. In: Proceedings of the International Workshop on Information Security and Application. pp. 319-321.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Bio-inspired Swarm Intelligence Meta-heuristic