A refined differential evolution algorithm based fuzzy classifier for intrusion detection

European Journal of Scientific Research

Volumn 65, Issue 2, 2011, Pages 246-259

  Victoire, T Amalraj ^a   Sakthivel, M ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

Differential evolution; Intrusion detection; KDD cup data set; Local search

Indexed keywords

EID: 83455235978     PISSN: 1450216X     EISSN: 1450202X     Source Type: Journal    
DOI: None     Document Type: Article

References (37)

1. Abe, S., & Lan, M.-S. (1995). A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Transactions on Fuzzy Systems, 3(1), 18-28
2. Anderson, D., Lunt, T.F., Javitz, H., Tamaru, A., Valdes, A., 1995. Detecting unusual program behavior using the statistical component of the next-generation intrusion detection expert system (NIDES).SRI-CSL-95-06, SRI International, Menlo Park, CA
3. Cannady, J. (1998). Artificial neural networks for misuse detection. In National information systems security conference (pp. 368-381
4. Cervantes, A., Galvan, I., & Isasi, P. (2005). A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm. In The 2005 IEEE congress on evolutionary computation (pp. 290-297
5. Cho, Sung-Bae (2002). Incorporating soft computing techniques into a probabilistic intrusion detection system. IEEE Transactions on Systems, Man and Cybernetics, Part C, 32(2), 154-160 (Pubitemid 35289399)
6. Cordon, O., Gomide, F., Herrera, F., Hofmann, F., & Magdalena, L. (2004). Ten years of genetic fuzzy systems current framework and new trends. Fuzzy Sets and Systems, 141, 5-31
7. Dasgupta, D., & González, F. (2002). An immunity-based technique to characterize intrusions in computer networks. IEEE Transactions on Evolutionary Computation, 6(3
8. Debar, H., & Dorizzi, B. (1992a). An application of a recurrent network to an intrusion detection system. In Proceedings of the international joint conference on neural networks (pp. 78-83
9. De Falco, I., Cioppa, A. D., & Tarantino, E. (2002). Discovering interesting classification rules with genetic programming. Applied Soft Computing, 23, 1-13
10. Elkan, C. (2000). Results of the KDD-99 classifier learning. ACM SIGKDD Explorations, 1, 63-64
11. Fan, W., Lee, W., Miller, M., Stolfo, S. J., & Chan, P. K. (2001). Using artificial anomalies to detect unknown and know network intrusions. In Proceedings of the first IEEE international conference on data mining
12. Gomez, J., & Dasgupta, D. (2001). Evolving fuzzy classifies for intrusion detection. In Proceedings of the 2002 IEEE information assurance workshop
13. Guan, Y., Ghorbani, A. A. & Belacel, N. (2003). Y-MEANS: A clustering method for intrusion detection. In Canadian conference on electrical and computer engineering (pp. 1083-1086
14. Harmer, P. K., Williams, P. D., Gunsch, G. H., & Lamont, G. B. (2002). An artificial immune system architecture for computer security applications. IEEE Transactions on Evolutionary Computation, 6(3
15. Idris, N. B., & Shanmugam, B. (2005). Artificial intelligence techniques applied to intrusion detection. Annual IEEE INDICON, 52-55 (Pubitemid 46276047)
16. Ilgun, K., 1993. USTAT: A real-time intrusion detection system for UNIX.In: Proceedings of the 1993 Computer Society Symposium on Research in Security and Privacy, Oakland, CA, 24-26 May, IEEEComputer Society Press, Los Alamitos, CA, pp. 16-29
17. Ishibuchi, H., Nakashima, T., & Murata, T. (2001). Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences, 109-133 (Pubitemid 32621497)
18. KDD-Cup data set: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
19. Kruegel, C., & Vigna, G. (2003). Anomaly detection of web-based attacks. In Proceedings of 10th ACM conference on computer and communications security (CCS '03) (pp. 251-261 (Pubitemid 40673807)
20. Lee, S.C., Heinbuch, D.V., 2001. Training a neural-network based intrusion detector to recognize novel attacks. IEEE Trans. Syst. Man Cybernet. A 31, 294-299 (Pubitemid 33142055)
21. Lee, W., Salvatore, J. S., & Mok, K. W. M. (1998). Mining audit data to build intrusion detection models. In Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (pp. 66-72
22. Leon, E., Nasraoui, O., & Gomez, J. (2004). Anomaly detection based on unsupervised niche clustering with application to network intrusion detection. In Proceedings of IEEE conference on evolutionary computation (CEC) (pp. 502-508
23. Lincoln Laboratory MIT. http://www.ll.mit.edu
24. Lunt, T., Tamaru, A., Gilham, F., Jagannathan, R., Jalali, C., Neumann, P.G., Javitz, H.S., Valdes, A., Garvey, T.D., 1992.A real time intrusion detection expert system (IDES).Final Report.SRI International, Menlo Park, CA
25. Mohammad Saniee Abadeh, Jafar Habibi, Zeynab Barzegar, Muna Sergi (2007), A parallel genetic local search algorithm for intrusion detection, Engineering Applications of Artificial Intelligence 20 (8) 1058-1069 (Pubitemid 350016848)
26. Oh, S. H., & Lee, W. S. (2003). An anomaly intrusion detection method by clustering normal user behavior. Computers & Security, 22(7), 596-612
27. Ozyer, T., Alhajj, R., & Barker, K. (2007). Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening. Journal of Network and Computer Applications, 30, 99-113 (Pubitemid 44666485)
28. Provost, F., Fawcett, T., 1998.The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning
29. Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster back propagation learning: The RPROP algorithm. In Proceedings of the IEEE international conference on neural networks, San Francisco
30. Ryan, J., Lin, M., & Miikkulainen, R. (1998). Intrusion detection with neural networks. Advances in neural information processing systems (Vol. 10). Cambridge, MA: MIT Press
31. Saniee Abadeh, M., Habibi, J., & Lucas, C. (2007). Intrusion detection using a fuzzy geneticsbased learning algorithm. Journal of Network and Computer Applications, 30(1), 414-428 (Pubitemid 44666474)
32. Xu, B., & Zhang, A. (2005). Application of support vector clustering algorithm to network intrusion detection. In International conference on neural networks and brain 2005, ICNN&B '05 (Vol. 2, pp. 1036-1040
33. Xu, B., & Zhang, A. (2005). Application of support vector clustering algorithm to network intrusion detection. In International conference on neural networks and brain, 2005, ICNN&B '05 (Vol. 2, pp. 1036-1040
34. Yamanishi, K., Takeuchi, J.-I., & Williams, G. (2000). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 320-324
35. Yang, X. R., Shen, J. Y., & Wang, R. (2002). Artificial immune theory based network intrusion detection system and the algorithms design. In Proceedings of 2002 international conference on machine learning and cybernetics. Beijing (pp. 73-77
36. Ye, N., Chen, Qiang, & Borror, Connie M. (2004). EWMA forecast of normal system activity for computer intrusion detection. IEEE Transactions on Reliability, 53(4
37. Ye, N., Vilbert, S., & Chen, Q. (2003). Computer intrusion detection through EWMA for autocorrelated and uncorrelated data. IEEE Transactions on Reliability, 52(1).