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Combining Heterogeneous Classifiers for Network Intrusion Detection

  • Ali Borji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4846)

Abstract

Extensive use of computer networks and online electronic data and high demand for security has called for reliable intrusion detection systems. A repertoire of different classifiers has been proposed for this problem over last decade. In this paper we propose a combining classification approach for intrusion detection. Outputs of four base classifiers ANN, SVM, kNN and decision trees are fused using three combination strategies: majority voting, Bayesian averaging and a belief measure. Our results support the superiority of the proposed approach compared with single classifiers for the problem of intrusion detection.

Keywords

Intrusion Detection Combined Classifiers PCA Misuse Detection Anomaly Detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ali Borji
    • 1
  1. 1.School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics, Niavaran Bldg. P.O.Box 19395-5746, TehranIran

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