Improving Multiclass Classification in Intrusion Detection Using Clustered Linear Separator Analytics

This research proposes a new ensemble classification technique called Clustered Linear Separator Analytic (CLSA) for intrusion detection system. The approach makes use of both linear separation technique as well as cluster based approach. Genetic Linear Discriminant Analysis (GLDA) is used for features transformation and optimum subset selection while CLSA is used for classification. CLSA is a much improved version of SVM and LDA as it performed both binary and multi-class classification when combined rather than when employed independently. Comparative experiments were performed with SVM (Support Vector Machine) with its variant kernels and CLSA to evaluate the robustness and classification accuracy of the proposed approach. Detection rate (DR) were used to evaluate the performance of the proposed system. The results indicate that the combination of these approaches produce an efficient IDS compared with state-of-the-art approaches.