Empirical study on fusion methods using ensemble of RBFNN for network intrusion detection

Authors:
Aki P. F. Chan;Daniel S. Yeung;Eric C. C. Tsang;Wing W. Y. Ng
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

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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.