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)


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.


False Alarm Rate Majority Vote Intrusion Detection Base Classifier Radial Basis Function Neural Network 
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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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