Application of feature selection and fuzzy ARTMAP to intrusion detection

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn 6, Issue , 2006, Pages 4880-4885

  Vilakazi, Christina B ^a   Marwala, Tshilidzi ^a  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; FEATURE EXTRACTION; GENETIC ALGORITHMS; INTRUSION DETECTION;

COMPUTATION TIME; OPTIMAL VIGILANCE PARAMETERS; SEQUENTIAL BACKWARD FLOATING SEARCH;

FUZZY CONTROL;

EID: 34548136243     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2006.385078     Document Type: Conference Paper

References (27)

1. S. Mukkamala and A.H. Sung, Feature Selection for Intrusion Detection using NN and SVM, Journal of the Transport Research Board National Acada, Transport Research Record no 18822, 2003, pp.33-39.
2. Z. Huang, M. Pei, E. Goodman, Y. Huang and G. Li, Genetic algorithm optimized feature transformation: a comparison with different classifiers, In Proc. GECCO 2003, 2003, pp. 2121-2133.
3. M. Pei, E.D. Goodman and W.F. Punch, Feature extraction using genetic algorithms.In Proc. Int'l Symp. On Intelligent Data Engineering and Learning, 1998, pp. 371-384.
4. W.F. Punch, E.D. Goodman, M. Pei, L. Chia-Shun, P. Hovland and R. Enbody, Further research on feature selection and classification using genetic algorithms. In Proc. 5th Int'l Conf. Genetic Algorithms, July 1993, pp. 557.
5. T. Gartner and P.A. Flach, WBCsvm: Weighted Bayesian Classification based on support vector machine,In Proc. 18th International Conference on Machine Learning, 2001, pp. 156-161.
6. C. Sriletha, A. Ajith and PT. Johnson, Feature deduction and ensemble design of Intrusion Detection System, Journal of Computer and security, vol. 24, 2005, pp.295-307.
7. K. Ilgun, USTAT: a real-time intrusion detection system for UNIX, Proceedings of the 1993 Computer Society Symposium on Research in Security and Privacy, 1993, pp. 16-29.
8. D. Anderson, T.F. Lunt, H. Javitz, A. Tamaru and A. Valdes, Detecting unusual program behavior using the stastistical component of the nextgeneration intrusion detection expert system (NIDES), SRI-CSL-95-06, Menlo Park, CA: SRI International; 1995.
9. A. Porras and P.G. Neumann, EMERALD: Event monitoring enabling responses to anomalous live disturbances, In Proceedings of the National Information Systems Security Conference, 1997, pp. 353-65.
10. H. Debar, B. Beck and D. Siboni, A neural network component for an Intrusion Detection System, In Proceedings of IEEE Computer Society symposium on Research in Security and Privacy, 1992, pp. 240-250.
11. H. Debar and B. Dorizzi, An application of a recurrent network to an Intrusion Detection System, In Proceedings of IJCNN, 1992, pp.78-83.
12. J. Ryan, M.J. Lin and R. Mikkulalan, Intrusion Detection on NN, Advances in Neural Information Processing System, Cambridge MA, MIT Press, 1997.
13. S. Mitra and S.K. Pal, SOM neural network as a fuzzy classifier, IEEE Transaction of Systems, Man and Cybernetics, vol. 24, no.3 pp. 385-399, 1994.
14. S. Mukkamala, A. Jonaski and A.T. Sung, Intrusion Detection using neural network and SVM, Proceedings of the IEEE IJCNN, 2002, pp. 202-207.
15. S. Mukkamala, A.H. Sung and A. Ajith, Intrusion Detection using intelligent paradigm,Journal of Network and Computer Application, vol. 28, 2005, pp. 167-182.
16. C. Zhong, J. Jiong and M. Kanel, Intrusion Detection using hierachical NN, Pattern Recognition Letters, vol. 26,2005, pp. 779-791.
17. E. Marais and T. Marwala, Predicting global Internet instability caused by worms using Neural Networks, In Proc of the Annual Symposium of the Pattern Recognition Association of South Africa, South Africa, 2004, pp. 81-85.
18. W. Hu, Y. Liao, V. Rao and R.R. Venurri, Robust SVM for anomaly detection in Computer Security, In Proceedings of the 2003 International Conf. on MC and Application, LA California, 2003.
19. Z. Zhang and H. Shen, Application of Online training SVM for real time intrusion detection,Compuier Communications,vol.28, 2005, pp.14281442.
20. G. Li, J. Yang, C. Yo and D. Geng, Degree prediction of malignancyin brain glioma using support vector machine, Computers in Biology and Machine, vol. 36, 2006, pp.313-325.
21. B. Somol and P. Pudil, J. Novovicova and P. Paclik, Adaptive floating search methods in feature Selection, Pattern Recognition Letters, vol. 3, 1998, pp. 1157- 1163.
22. G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynold and D.B. Rosen, Fuzzy ARTMAP: A neural network for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Network, vol.3, no.5, 1992, pp. 689-713.
23. G.A. Carpenter, Default ARTMAP, In Proceedings 2003 IJCNN, 2003, pp. 1396-1401.
24. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Berlin: Springer, third, revised and extended ed., 1999.
25. D. E. Goldberg. Genetic Algorithms In Search, Optimisation and Machine Learning, New York: AddisonWesley Publishing Company, Inc., 1998
26. D.J. Newman, S. Hettich, C.L. Blake and C.J. Merz, UCI Respository of machine learning databases, http://www.ics.uci.edu/ mlearn/MLRepository.html, University of carlifornia, Irvine, Department of Information and Computer Science, 1998.
27. K. Kendal, A database of Computer Attacks for the Evaluation of Intrusion Detection System, BSc and MSc Thesis in Computer Science and Engineering, MIT, 1999.