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Empirical Study on Fusion Methods Using Ensemble of RBFNN for Network Intrusion Detection

  • Aki P. F. Chan
  • Daniel S. Yeung
  • Eric C. C. Tsang
  • Wing W. Y. Ng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

The network security problem has become a critical issue and many approaches have been proposed to tackle the information security problems, especially the Denial of Service (DoS) attacks. Multiple Classifier System (MCS) is one of the approaches that have been adopted in the detection of DoS attacks recently. Fusion strategy is crucial and has great impact on the classification performance of an MCS. However the selection of the fusion strategy for an MCS in DoS problem varies widely. In this paper, we focus on the comparative study on adopting different fusion strategies for an MCS in DoS problem.

Keywords

False Alarm Rate Majority Vote Intrusion Detection Base Classifier Radial Basis Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chan, A.P.F., Ng, W.W.Y., Yeung, D.S., Tsang, E.C.C.: Multiple Classifier System with Feature Grouping for Intrusion Detection: Mutual Information Approach. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, pp. 141–148. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Giorgio, G., Fabio, R., Luca, D.: Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters 24, 1795–1803 (2003)CrossRefGoogle Scholar
  3. 3.
    Hanson, L., Salamon, P.: Neural Network Ensembles. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)CrossRefGoogle Scholar
  4. 4.
    Householder, A., Manion, A., Pesante, L., Weaver, G., Thomas, R.: Managing the Threat of Denial-of-Service Attacks. Carnegie Mellon CERT Coordination Center, Pittsburgh (2001)Google Scholar
  5. 5.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)CrossRefGoogle Scholar
  6. 6.
    Kumar, S., Spafford, E.H.: A pattern matching model for misuse intrusion detection. In: Proceedings of the 17th National Computer Security Conference, pp. 11–21 (1994b)Google Scholar
  7. 7.
    Kuncheva, L.I.: Switching between selection and fusion in combining classifiers: An experiment. IEEE Trans. on Systems, Man and Cybernetics, Part B 32, 146–156 (2002)CrossRefGoogle Scholar
  8. 8.
    Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Proceedings of the 7th USENIX Security Symposium (1998)Google Scholar
  9. 9.
    Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion Detection Using Ensemble of Soft Computing and Hard Computing Paradigms. Journal of Network and Computer Applications 28, 167–182 (2005)CrossRefGoogle Scholar
  10. 10.
    Ng, W.W.Y., Chan, A.P.F., Yeung, D.S., Tsang, E.C.C.: Quantitative Study on the Generalization Error of Multiple Classifier Systems. To appear in IEEE Proc. of International Conference on Systems, Man and Cybernetics, Hawaii, USA (October 2005)Google Scholar
  11. 11.
    Ng, W.W.Y., Chang, R.K.C., Yeung, D.S.: Dimensionality Reduction for Denial of Service Detection Problems Using RBFNN Output Sensitivity. In: IEEE Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1293–1298 (2003)Google Scholar
  12. 12.
    Rogova, G.: Combining the results of several neural network classifiers. Neural Networks 7, 777–781 (1994)CrossRefGoogle Scholar
  13. 13.
    Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifier Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78–87. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Tumer, K., Ghosh, J.: Classifier combining: analytical results and implications. In: National Conference on Artificial Intelligence (1996)Google Scholar
  15. 15.
    Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science 8, 385–404 (1996)CrossRefGoogle Scholar
  16. 16.
    Verwoerd, T., Hunt, R.: Intrusion detection techniques and approaches. Computer communications 25, 1356–1365 (2002)CrossRefGoogle Scholar
  17. 17.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on SMC 22, 418–435 (1992)Google Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aki P. F. Chan
    • 1
  • Daniel S. Yeung
    • 1
  • Eric C. C. Tsang
    • 1
  • Wing W. Y. Ng
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic UniversityHong KongChina

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