Network intrusion detection method by least squares support vector machine classifier

Abstract

Network is more and more popular in the present society. Least squares support vector machine is a kind modified support vector machine for classification, which can solve a convex quadratic programming problem. Least squares support vector machine is presented to network intrusion detection. We apply KDDCUP99 experimental data of MIT Lincoln Laboratory to research the classification performance of LS-SVM classifier. Support vector machine, BP neural network are used to compare with the proposed method in the paper. The experimental indicates that LS-SVM detection method has higher detection accuracy than support vector machine, BP neural network.