Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

Expert Systems with Applications

Volumn 38, Issue 6, 2011, Pages 7067-7075

  Abadeh, Mohammad Saniee ^a,b   Mohamadi, Hamid ^a   Habibi, Jafar ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^b TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Combinatorial problems; Fuzzy rule extraction; Genetic algorithms; Intrusion detection; Learning; Pattern recognition

Indexed keywords

ABNORMAL BEHAVIOR; APPROXIMATE REASONING; COMBINATORIAL PROBLEM; DATA SETS; DESIGN AND ANALYSIS; FUZZY RULE EXTRACTION; FUZZY RULE SET; GENETIC FUZZY SYSTEMS; HIGH-DIMENSIONAL; INTRUSION DETECTION SYSTEMS; LEARNING; LEARNING CAPABILITIES; MICHIGAN; PITTSBURGH; REAL-WORLD APPLICATION; RULE LEARNING;

COMBINATORIAL MATHEMATICS; FUZZY RULES; FUZZY SETS; FUZZY SYSTEMS; GENETIC ALGORITHMS; INTERNET; LEARNING ALGORITHMS; PATTERN RECOGNITION;

INTRUSION DETECTION;

EID: 79951578112     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.12.006     Document Type: Article

References (56)

1. S. Abe, and M.-S. Lan A method for fuzzy rules extraction directly from numerical data and its application to pattern classification IEEE Transactions on Fuzzy Systems 3 1 1995 18 28
2. Ahmed, A. A. E.; Traore, L. (2005). Anomaly intrusion detection based on biometrics. In Proceedings of the 2005 IEEE workshop on information assurance and security united states military academy, West Point, NY.
3. Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy. Technical report no. 99-15. Department of Computer Engineering, Chalmers University of Technology, Sweden.
4. Bridges, S. M. S.; Vaughn, R. B. (2000). Fuzzy data mining and genetic algorithms applied to intrusion detection. In Proceedings of the national information systems security conference.
5. Cannady, J. (1998). Artificial neural networks for misuse detection. In National information systems security conference (pp. 368-381).
6. 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).
7. Sung-Bae Cho Incorporating soft computing techniques into a probabilistic intrusion detection system IEEE Transactions on Systems, Man and Cybernetics, Part C 32 2 2002 154 160 (Pubitemid 35289399)
8. S. Cho, and S. Cha SAD: Web session anomaly detection based on parameter estimation Computers & Security 23 4 2004 265 351
9. S. Cho, and S. Cha SAD: Web session anomaly detection based on parameter estimation Computers & Security 23 4 2004 265 351
10. O. Cordon, F. Gomide, F. Herrera, F. Hofmann, and L. Magdalena Ten years of genetic fuzzy systems current framework and new trends Fuzzy Sets and Systems 141 2004 5 31
11. D. Dasgupta, and F. González An immunity-based technique to characterize intrusions in computer networks IEEE Transactions on Evolutionary Computation 6 3 2002
12. 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).
13. Debar, H.; Becke, B.; Siboni, D. (1992b). A neural network component for an intrusion detection system. In Proceedings of the IEEE computer society symposium on research in security and privacy (pp. 240-250). (Pubitemid 23569177)
14. I. De Falco, A.D. Cioppa, and E. Tarantino Discovering interesting classification rules with genetic programming Applied Soft Computing 23 2002 1 13
15. C. Elkan Results of the KDD-99 classifier learning ACM SIGKDD Explorations 1 2000 63 64
16. 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.
17. Feng, Y.; Wu, Z.-F.; Wu, K.-G.; W.; Xiong, Z.-Y.; Zhou, Y. (2005). An unsupervised anomaly intrusion detection algorithm based on swarm intelligence. In Proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou (pp. 18-21).
18. Fox, K. L.; Henning, R. R.; Reed, J. H.; Simonian, R. (1990). A neural network approach towards intrusion detection. In Proceedings of the national security conference, Washington DC (pp. 125-134).
19. Gao, H.-H.; Yang, H.-H.; Wang, X.-Y. (2005). Ant colony optimization based network intrusion feature selection and detection. In Proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou (pp. 18-21).
20. Gomez, J.; Dasgupta, D. (2001). Evolving fuzzy classifies for intrusion detection. In Proceedings of the 2002 IEEE information assurance workshop.
21. 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).
22. P.K. Harmer, P.D. Williams, G.H. Gunsch, and G.B. Lamont An artificial immune system architecture for computer security applications IEEE Transactions on Evolutionary Computation 6 3 2002
23. Heady, R.; Luger, G.; Maccabe, A.; Servilla, M. (1990). The architecture of network level intrusion detection system. Technical Report. Department of Computer Science, University of New Mexico.
24. F. Hofmann Combining boosting and evolutionary algorithms for learning of fuzzy classification rules Fuzzy Sets and Systems 2004 47 58
25. S.A. Hofmeyr, S. Forrest, and A. Somayaji Intrusion detection using sequences of system calls Journal of Computer Security 6 1998 151 180
26. Y.-C. Hu, R.-S. Chen, and G.-H. Tzeng Finding fuzzy classification rules using data mining techniques Pattern Recognition Letters 24 2003 509 519 (Pubitemid 36080792)
27. N.B. Idris, and B. Shanmugam Artificial intelligence techniques applied to intrusion detection Annual IEEE INDICON 2005 52 55 (Pubitemid 46276047)
28. H. Ishibuchi, T. Nakashima, and T. Murata Three-objective genetics-based machine learning for linguistic rule extraction Information Sciences 2001 109 133 (Pubitemid 32621497)
29. H. Ishibuchi, K. Nozaki, and H. Tanaka Distributed representation of fuzzy rules and its application to pattern classification Fuzzy Sets and Systems 52 1 1992 21 32
30. H. Ishibuchi, and T. Yamamoto Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining Fuzzy Sets and Systems 141 2004 59 88
31. H. Ishibuchi, T. Yamamoto, and T. Nakashima Hybridization of fuzzy GBML approaches for pattern classification problems IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 35 2 2005
32. KDD-Cup data set: .
33. 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)
34. C.C. Lee Fuzzy logic in control systems: Fuzzy logic controller, Part I and Part II IEEE Transactions on Systems, Man, and Cybernetics 20 2 1990 404 435
35. 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).
36. 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).
37. Lincoln Laboratory MIT. .
38. S. Mitra, and S.K. Pal Self-organizing neural network as a fuzzy classifier IEEE Transactions on Systems, Man, and Cybernetics 24 3 1994 385 399
39. Mukkamala, S.; Sung, A. H. (2003). Feature selection for intrusion detection using neural networks and support vector machines. Journal of the Transport Research Board National Academy. Transport research record no. 1822, pp. 33-39.
40. Murali, A.; Rao, M. (2005). A Survey on intrusion detection approaches. In First international conference on information and communication technologies (pp. 233-240.
41. S.H. Oh, and W.S. Lee An anomaly intrusion detection method by clustering normal user behavior Computers & Security 22 7 2003 596 612
42. T. Ozyer, R. Alhajj, and K. Barker Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening Journal of Network and Computer Applications 30 2007 99 113 (Pubitemid 44666485)
43. 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.
44. Rouwhorst, S. E.; Engelbrecht, A. P. (2000). Searching the forest: Using decision trees as building blocks for evolutionary search in classification databases. In Proceedings of IEEE congress evolutionary computation (Vol. 1, pp. 633-638).
45. J. Ryan, M. Lin, and R. Miikkulainen Intrusion detection with neural networks Advances in neural information processing systems Vol. 10 1998 MIT Press Cambridge, MA
46. M. Saniee Abadeh, J. Habibi, and C. Lucas Intrusion detection using a fuzzy genetics-based learning algorithm Journal of Network and Computer Applications 30 1 2007 414 428 (Pubitemid 44666474)
47. M. Sugeno An introductory survey of fuzzy control Information Sciences 36 1/2 1985 59 83
48. K.C. Tan, Q. Yu, C.M. Heng, and T.H. Lee Evolutionary computing for knowledge discovery in medical diagnosis Artificial Intelligence in Medicine 27 2003 129 154 (Pubitemid 36294091)
49. Tian, J.-F.; Ying X.; Yue F.; Wang, J.-L. (2005). Intrusion detection combining multiple decision trees by fuzzy logic. In Sixth international conference on parallel and distributed computing, applications and technologies (pp. 256-258). (Pubitemid 43897696)
50. L.X. Wangm, and J.M. Mendel Generating fuzzy rules by learning from examples IEEE Transactions on Systems, Man, and Cybernetics 22 6 1992 1414 1427
51. 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).
52. 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).
53. 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).
54. 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).
55. N. Ye, Qiang Chen, and Connie M. Borror EWMA forecast of normal system activity for computer intrusion detection IEEE Transactions on Reliability 53 4 2004
56. N. Ye, S. Vilbert, and Q. Chen Computer intrusion detection through EWMA for autocorrelated and uncorrelated data IEEE Transactions on Reliability 52 1 2003