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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Borji, A. (2007). Combining heterogeneous classifiers for network intrusion detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4846 LNCS, pp. 254–260). Springer Verlag. https://doi.org/10.1007/978-3-540-76929-3_24
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