Combining heterogeneous classifiers for network intrusion detection

Authors:
Ali Borji
Affiliations:
School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran
Venue:
ASIAN'07 Proceedings of the 12th Asian computing science conference on Advances in computer science: computer and network security
Year:
2007

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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.