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ASIAN
2007
Springer

Combining Heterogeneous Classifiers for Network Intrusion Detection

13 years 9 months ago
Combining Heterogeneous Classifiers for Network Intrusion Detection
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.
Ali Borji
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ASIAN
Authors Ali Borji
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