A SVM Method for P2P Traffic Identification based on Multiple Traffic Mode
Support Vector Machines (SVM) algorithms are one of the algorithm s currently applied in Deep Traffic Inspection (DFI) technologies. This paper realizes online real-time traffic information detection, provides a P2P traffic identification system that supports online SVM analysis and offline SVM training function, and demonstrates the thinking of different identification for IP data traffic and IP-Port data traffic. This paper designs different combinations of traffic features for IP data traffic and IP-Port data traffic, analyzes the effectiveness and exactness of these combinations from various function criteria, and based on a lot of experiments, obtains a best SVM kernel function and a combination of parameters that match es the very combination of traffic features.