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.