Intrusion detection model based on particle swarm optimization and support vector machine

Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA 2007

Volumn , Issue , 2007, Pages 186-192

  Srinoy, Surat ^a,b  

^a IEEE

^b SUAN DUSIT UNIVERSITY   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION TECHNOLOGY; NETWORK SECURITY; PROBLEM SOLVING; SUPPORT VECTOR MACHINES; TELECOMMUNICATION TRAFFIC;

CLASSIFICATION ACCURACY; FALSE ALARMS; FITNESS FUNCTION; INTRUSION DETECTION SYSTEMS; PARTICLE SWARM OPTIMIZATION;

INTRUSION DETECTION;

EID: 34548793095     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISDA.2007.368152     Document Type: Conference Paper

References (35)

1. D.S Bauer, M.E Koblentz,. NIDX- an expert system for real-time network intrusion detection, Proceedings of the Computer Networking Symposium, 1988. pp. 98-106.
2. Herv Debar, Maro Dacier, and Andreas Wespi, "Towards a Taxonomy of Intrusion Detection Systems", IBM Technical Paper, Computer Networks, Vol.39, Issue 9, pp. 805-822, April 1999.
3. R. Bace and P. Mell, "Intrusion Detection Systems", NIST Special Publication on Intrusion Detection System, 31 November 2001.
4. A.Sundaram, "An introduction to intrusion detection, Crossroads": The ACM student magazine, 2(4), April 1996.
5. D. Denning, "An intrusion-detection model", In IEEE computer society symposium on research in security and privacy, 1986, pp.118-131.
6. T.Lane, "Machine Learning techniques for the computer Security", PhD thesis, Purdue University, 2000.
7. Madori Asaka, Takefumi Onabuta, Tadashi Inoue, Shunji Okazawa, and Shigeki Goto, "A new intrusion detection method, based on discriminant analysis", Oakland, pp. 130-143, May 2001.
8. Ming Tang, Song-Can Chen, Yi Zhuang and Jia Liu, "Using Statistical Analysis and Support Vector Machine Classification to Detection Complicated Attacks", Proc. Of the Third International Conference on Machine, Learning and Cybernetics IEEE Computer Society, Shanghai, China, August 26-29, 2004.
9. Furey, T.S., Cristianini, N., Duffy, N., Beddarski, K.W., Schummer, M. and Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, vol. 16, pp.906-914, 2000.
10. Guyon, I., Weston, J., Barnhill, S. and Vapnik, V., "Gene selection for cancer classification using support vector machines," Machine Learning, Vol. 46, pp.389-422, 2002.
11. Lee, Y. and Lee, C.-K., "Classification of multiple cancer types be multicategory support vector machines using gene expression data," Bioinformatics, vol. 18, pp. 1132-1139, 2003.
12. Chung-Jui Tu, Li-Yeh Chuang, Jun-Yang Chang, and Cheng-Hong Yang, "Fearture Selection using PSO-SVM", in Proc. International multiconf. Engineers and computer scientist, Hong Kong, 2006, pp.138-143.
13. S. Chebrolu, A. Abraham, J. P., Thomas, Feature Deduction and Ensemble Design of Intrusion Detection Systems, Computer & Security, 2004.
14. J. Twycross, Immune Systems, "Danger Theory and Intrusion Detection", presented at the AISB 2004 Symposium on Immune System and Cognition, Leeds, U.K., March 2004.
15. R.T. Alves, M.R.B.S. Delgado, H.S. Lopes, A.A. Freitas, "An artificial immune system for fuzzy-rule induction in data mining", Lecture Notes in Computet Science, Berlin: Springer-Verlag, Vol.3242, 2004, pp. 1011-1020.
16. W. Lee and S., Stolfo, Data Mining Approaches for Intrusion Detection, Proceedings of the 7th USENIX security symposium, 1998.
17. J. Kennedy, R.C. Eberhart, Particle Swarm Optimisation, in: Proceedings of the IEEE, International Conference on Neural Networks, Piscataway, 1995.
18. Stone, M., "Cross-Validation choice and assessment of statistical predictions," Journal of the Royal Statistical Society B, vol 36, pp. 111-147, 1974.
19. Kennedy, J., Eberhart, R.C., "A discrete binary Version of the particle swarm algorithm", Systems, Man, and Cyemetics, 1997. 'Computational Cybernetics and Simulation]., 1997 IEEE international Conference on Volume 5, 12-15 Oct. 1997. pp.4104-4108.
20. Frieß, T., N. Cristianini, and C. Campbell, "The Kernel-Adatron: a Fast and Simple Learning Procedure for Suuport Vector machines," Proc. Of the Fifteenth Internaltional Conference on Machine Learning, pp. 188-196, 1998.
21. Drucker, H., Burges, C., Kaufman, L., Smola, A. and Vapnik, "Support Vector regression Machines, " in Moser, M., Jordan, M., and Petshe, T. (ed.) Neural Information Processing Systems, Vol. 9. MIT Press, Cambridge, Ma, pp. 155-161. 1997.
22. Vapnik, V.N., The Nature of Statistical Learning Theory, Springer Verlag, 1995.
23. Anlauf, J.K., and Biehl, M., "The Adatron-an adaptive perceptron algorihm," Europhysics letters, 10, pp., 687-692. 1989.
24. Opper, M., "Learning Time of Neural Networks; Exact Solution for a Perceptron Algorithm," physical review A38:3824. 1988.
25. Boser, B., Guyon, I., Vapnik, "A training algorithm for optimal margin classifiers," Fifth Annual Workshop on Computational Learning Theory, ACM Press. 1988.
26. Colin, C. and Nello, C., "Simple Learning. Algorithms for Training: Support Vector Machines," 1998.
27. Scholkopf, B. and Smola, Learning with Kernels: support Vector Machines, Regularization, Optimization and Geyond. MIT Press, Cambridge, MA
28. KDD data set, 1999; http://kdd.ics.uci.edu/databases/kddcup99/kddcup99. html
29. P. Laskov, K. Rieck, C. Schäfer, K.R. Müller, "Visualization of anomaly detection, using prediction sensitivity". Proc. of Sicherheit, April.2005, 197-208.
30. Math Works, Statistical Toolbox for User's-Guide, Math Works, 2001.
31. Steven R. Gunn, "Support Vector Machines for Classification and Regression," Technical report, 1998.
32. W. Chimphlee, Abdul Hanan Abdullah, Mohd Noor Md Sap and S. Chimphlee, "Unsupervised Anomaly Detection, with Unlabeled Data Using Clustering", Proc. Int. Conf. on ICT-Mercu Buana ICT2005. 42-49.
33. H. Jin, J. Sun, H. Chen, and Z. Han, A Fuzzy Data Mining Based Intrusion Detection System, Proc. of 10thInternational Workshop, an future Trends in Distributed Computing Systems (FTDCS04). IEEE Computer Society, Suzhou, China, May 26-28, 2004, 191-197.
34. A. Lazareyic, A. Ozgur, L. Ertoz, J. Srivastava, and V., Kumar, A comparative study of anomaly detection schemes in network intrusion detection. In SIAM International Conference on Data-Mining, 2003.
35. T. Wakaki, H, Itakura, and M.Tamura, Rough Set-Aided Feature Selection for Automatic Web-Page Classification, Proc. of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'04)