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.

• Type: Conference
• Year: 2007
• Where: ASIAN
• Authors: Ali Borji
• Keywords: Algorithms, ASIAN 2007, Intrusion Detection, Online Electronic Data, Reliable Intrusion Detection