An Effective Feature Selection Approach for Network Intrusion Detection

Abstract

Processing huge amounts of network data is one of the largest challenges for network-based intrusion detection system (IDS). Usually these data contain lots of irrelevant or redundant features. To improve the efficiency of IDS, relevant features are necessary to be extracted from original data via feature selection approaches. In this paper, an effective feature selection approach based on Bayesian Network classifier is proposed. And with the same intrusion detection benchmark dataset (NSL-KDD), the performance of the proposed approach is evaluated and compared with other commonly used feature selection methods. It is shown by empirical results that features selected by our approach have decreased the time to detect attacks and increased the classification accuracy as well as the true positive rates significantly.