With the increasingly serious security risks in cyberspace,the research of network anomaly detection technology based on traffic has gained more concern.In view of various network traffic modes,variable working conditions,complex correlation and other challenges,this paper proposes an SVM-based abnormal detection method for network traffic.Firstly,multiple dimensions of multidimensional network traffic data are sorted according to their importance,and the top 20 dimensions with the greatest importance are selected to reconstruct the data set.Secondly,part of the data is selected to select the optimal parameters of SVM through grid search function.Finally,an anomaly detection model is established according to the optimal parameters,and the performance of the model is tested through the open data set.The results of comparison with many different methods show that both 98％precision and excellent detection performance are achieved.
NETL
NSTL
万方数据
