Multiple classifier system with feature grouping for intrusion detection: mutual information approach

The information security of computer networks has become a serious global issue and also a hot topic in computer networking researches. Many approaches have been proposed to tackle these problems, especially the denial of service (DoS) attacks. The Multiple Classifier System (MCS) is one of the approaches that have been adopted in the detection of DoS attacks recently. For a MCS to perform better than a single classifier, it is crucial for the base classifiers which embedded in the MCS to be diverse. Both resampling, e.g. bagging, and feature grouping could promote diversity of base classifiers. In this paper, we propose an approach to select the reduced feature group for each of the base classifiers in the MCS based on the mutual information between the features and class labels. The base classifiers being combined using the weighted sum is examined in this paper. Different feature grouping methods are evaluated theoretically and experimentally.