Abstract
This paper tries to propose a fuzzy particle optimization algorithm (FPSO) for intrusion detection. The proposed FPSO classifier works on a knowledge base modelled as a fuzzy rule if-then and improved by a PSO algorithm. The objective is to obtain good quality solutions by optimizing the fuzzy rules generation. The method is tested on the benchmark KDD’99 intrusion dataset and compared with the fuzzy genetic algorithm and with other existing techniques available in the literature. The obtained results show the efficiency of the proposed approach.
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References
Abadeha, S., Habibia, J., Lucasb, C.: Intrusion detection using a fuzzy genetics- based learning algorithm. Journal of Network and Computer Applications 30, 414–428 (2007)
Abadeha, S., Habibia, J., Soroush, E.: Induction of Fuzzy Classification systems via evolutionary ACO-based algorithms. IJSSST 9(3) (September 2008)
Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Ben-Amor, N., Benferhat, S., Elouedi, Z.: Naive Bayes vs Decision Trees in Intrusion Detection Systems. In: Proceedings of the ACM Symposium on Applied Computing, pp. 420–424. ACM Press (2004)
Boughaci, D., Bouhali, S., Ordeche, S.: A Fuzzy Local Search for intrusion detection. In: Proceedding of the ACIT (2011)
Boughaci, D., Herkat, M. L., Lazzazi, M.A.: A specific fuzzy genetic Algorithm for intrusion detection. In: Proceedings of ICCIT (2012)
Boughaci, D., Drias, H., Bendib, A., Bouznit, Y., Benhamou, B.: Distributed Instrusion Detection Framework Based on Mobile Agents. In: Proceedings of the International Conference on Dependability of Computer Systems, pp. 248–255. IEEE Press (2006)
Debar, H., Becker, M., Siboni, D.: A neural network component for an intrusion detection system. In: Proceedings of the IEEE Symposium of Research in Computer Security and Privacy, pp. 240–250 (May 1992)
Nath, K.G.B., Ramamohanarao, K.: Layered Approach Using Conditional Random Fields for Intrusion Detection. IEEE Trans. Dependable Sec. Comput. 7(1), 35–49 (2010)
Gao, M., Zhou, M. C.: Fuzzy intrusion detection based on fuzzy reasoning Petri Nets. In: Proceeding of the 2003 IEEE International Conference on Systems, Man and Cybernetics, 5-8, Washington D.C., pp. 1272-1277 (October 2003)
Gomez, J., Dasgupta, D.: Evolving Fuzzy Classifies for Intrusion Detection. In: Proceedings of the 2002 IEEE Information Assurance Workshop (2002)
Ishibuchi, H., Murata, T.: Techniques and applications of genetic algorithms-based methods for designing compact fuzzy classification systems. Fuzzy Theory Systems Techniques and Applications 3(40), 1081–1109 (1999)
Javitz, H.S., Valdes, A., Lunt, T.F., Tamaru, A., Tyson, M., Lowrance, J.: Next generation intrusion detection expert system (NIDES). Technical Report A016- Rationales, SRI (1993)
John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)
Kennedyand, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks IV, pp. 1942–1948 (1995)
Kumar, S., Spafford, E.H.: A pattern-matching model for misuse intrusion detection. In: Proceedings of the International Computer Security Conference, pp. 11–21 (1994)
Lee, W., Stolfo, S., Mok, K.: Mining Audit Data to build Intrusion Detection Models. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 66–72. AAAI Press (1998)
Lunt, T.F., Jagannathan, R.: A prototype real-time intrusion-detection expert system. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 59–66 (1988)
Ludovic, M.: GASSATA, A genetic algorithmas an alternative tool for security audit trails analysis. In: First International Workshop on the Recent Advances in Intrusion Detection (1998)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Shah, H., Undercoffer, J., Joshi, A.: Fuzzy Clustering for Intrusion Detection. In: Proceedings of the 12th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1274–1278. IEEE Press (2003)
Vaccaro, H.S., Liepins, G.E.: Detection of anomalous computer session activity. In: Proceedings of the IEEE Symposium on Security and Privacy (May 1989)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
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Boughaci, D., Kadi, M.D.E., Kada, M. (2012). Fuzzy Particle Swarm Optimization for Intrusion Detection. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_64
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DOI: https://doi.org/10.1007/978-3-642-34500-5_64
Publisher Name: Springer, Berlin, Heidelberg
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