Network anomaly detection based on MRMHC-SVM algorithm

Network anomaly detection is the major direction of research in intrusion detection. Aiming at some problems, which include high false alarm rate, difficulties in obtaining exactly clean data for the modeling of normal patterns and the deterioration of detection rate because of some ldquonoisyrdquo data(unclean data) in the training set, in current intrusion detection techniques, we propose a novel network anomaly detection method based on MRMHC-SVM machine learning algorithm. The experimental results show that our method can effectively detect anomalies with high true positive rate and low false positive rate than the state-of-the-art anomaly detection methods. Moreover, the proposed method retains good detection performance after employing feature selection aiming at avoiding the ldquocurse of dimensionalityrdquo. In addition, even interfered by ldquonoisyrdquo data, it is robust and effective.