A SVM Method for P2P Traffic Identification based on Multiple Traffic Mode

Abstract

Support Vector Machines (SVM) algorithms are one of the algorithms 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 matches the very combination of traffic features.