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IPM
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YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 9588
School of Cognitive Sciences
  Title:   Combining Heterogeneous Classifiers for Network Intrusion Detection
  Author(s):  Ali Borji
  Status:   In Proceedings
  Proceeding: Lecture Notes in Computer Science, Asian
  Year:  2007
  Supported by:  IPM
  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, NN 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.

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