A genetic clustering method for intrusion detection

Pattern Recognition

Volumn 37, Issue 5, 2004, Pages 927-942

  Liu, Yongguo ^a,b   Chen, Kefei ^a   Liao, Xiaofeng ^b   Zhang, Wei ^b,c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b CHONGQING UNIVERSITY   (China)

^c CHONGQING UNIVERSITY OF EDUCATION   (China)

Author keywords

Clustering; Genetic algorithms; Intrusion detection; Simulated annealing

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; COMPUTER SIMULATION; DATA REDUCTION; LEARNING SYSTEMS;

GENETIC CLUSTERING; INTRUSION DETECTION;

PATTERN RECOGNITION;

ALGORITHM; CLUSTER ANALYSIS;

EID: 1842712339     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2003.09.011     Document Type: Article

References (29)

1. R. Heady, G. Luger, A. Maccabe, M. Servilla, The architecture of a network level intrusion detection system, Technical report CS90-20, University of New Mexico, 1990.
2. W. Lee, A data mining framework for constructing features and models for intrusion detection systems, Ph.D. Thesis, Columbia University, USA, 1999.
3. J. Allen, et al., State of the practice of intrusion detection technologies, Technical Report CMU/SEI-99-TR-028, Software Engineering Institute, Carnegie Mellon University, 1999.
4. Denning D.E. An intrusion-detection model IEEE Trans. Software Eng. 13 1987 222-232
5. D.E. Denning, P.G. Neumann, Requirements and model for IDES-A real-time intrusion detection system, Technical report, Computer Science Laboratory, SRI International, USA, 1985.
6. T. Lunt, Detecting intruders in computer systems, in: Proceedings of the 1993 Conference on Auditing and Computer Technology, Canada, 1993,
7. E. Eskin, Anomaly detection over noisy data using learned probability distributions, in: Proceedings of the International Conference on Machine Learning, USA, 2000, pp. 255-262.
8. W. Lee, S.J. Stolfo, K. Mok, Data mining in work flow environments: Experience in intrusion detection, in: Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, 1999, pp. 114-124.
9. W. Lee, S.J. Stolfo, Data mining approaches for intrusion detection, in: Proceedings of the 1998 USENIX Security Symposium, San Antonio, 1998, pp. 79-84.
10. Anderberg M.R. Cluster Analysis for Application 1973 Academic Press New York
11. Jain A.K. Dubes R. Algorithms for Clustering Data 1988 Prentice-Hall Englewood Cliffs, NJ
12. Selim S.Z. Ismail M.A. K-means-type algorithm: Generalized convergence theorem and characterization of local optimality IEEE Trans. Pattern Anal. Mach. Intell. 6 1984 81-87
13. Koontz W.L. Narendra P.M. Fukunaga K. A branch and bound clustering algorithm IEEE Trans. Comput. c-24 1975 908-915
14. Selim S.Z. Al-Sultan K.S. A simulated annealing algorithm for the clustering problem Pattern Recognition 24 1991 1003-1008
15. Klein R.W. Dubes R.C. Experiments in projection and clustering by simulated annealing Pattern Recognition 22 1989 213-220
16. Babu G.P. Murty M.N. Clustering with evolution strategies Pattern Recognition 27 1994 321-329
17. Murthy C.A. Chowdhury N. In search of optimal clusters using genetic algorithms Pattern Recognition Lett. 7 1996 825-832
18. Cucchiara R. Genetic algorithms for clustering in machine vision Mach. Vision Appl. 11 1998 1-6
19. Cowgill M.C. Harey R.J. A genetic algorithm approach to cluster analysis Comput. Math. Appl. 37 1999 99-106
20. Krishna K. Murty M.N. Genetic K-Means Algorithm IEEE Trans. Syst. Man Cybern. Part B 29 1999 433-439
21. Chiou Y.C. Lan L.W. Genetic clustering algorithms Eur. J. Oper. Res. 135 2001 413-427
22. Maulik U. Bandyopadhyay S. Genetic algorithm-based clustering technique Pattern Recognition 33 2000 1455-1465
23. N Inf. Sci. 146 2002 221-237
24. Bandyopadhyay S. Maulik U. Genetic clustering for automatic evolution of clusters and application to image classification Pattern Recognition 35 2002 1197-1208
25. Tseng L.Y. Yang S.B. A genetic approach to the automatic clustering problem Pattern Recognition 34 2001 415-424
26. Kim D.J. Park Y.W. Park D.J. A novel validity index for determination of the optimal number of clusters IEICE Trans. Inf. Syst. E84-D 2001 281-285
27. Michalewicz Z. Genetic Algorithm+Data=Structure=Evolution Program 1996 Springer New York
28. Kirpatrick S. Gelatt C.D. Vecchi M.P. Optimization by simulated annealing Science 220 1983 671-680
29. KDD99 cup dataset, http://kdd.ics.uci.edu/databases/kddcup99/kddcup1999.html, 1999.