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.
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Borji, A. (2007). Combining Heterogeneous Classifiers for Network Intrusion Detection. In: Cervesato, I. (eds) Advances in Computer Science – ASIAN 2007. Computer and Network Security. ASIAN 2007. Lecture Notes in Computer Science, vol 4846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76929-3_24
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DOI: https://doi.org/10.1007/978-3-540-76929-3_24
Publisher Name: Springer, Berlin, Heidelberg
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