X. Fang’s scientific contributions

Many redundant and irrelevant features not only slow training and classifying, increase computational consumption heavy but also reduce the accuracy of detection classification, especially when coping with big data. The features selection is an important issue for intrusion detection, an intrusion detection model based on SVM mixed with Fisher Criterion and SFS is proposed in this paper. In this method, the weights representing the relative importance of each feature are first obtained by Fisher Criterion. The features were joined to the feature subset one at a time, the ones with higher weight first, we run the classifier. Eventually, the optimized feature subset is obtained when a steady and satisfactory test result occurs. The performance of this algorithm is verified on MATLAB2012 platform in the KDD CUP 99 data, and the experimental results indicate good feasibility of the method.