A Peer-To-Peer Traffic Identification Method Using Machine Learning

The use of peer-to-peer (P2P) applications is growing dramatically, which results in several serious problems such as the network congestion and traffic hindrance. In this paper, a method is proposed to identify the P2P traffic based on the machine learning. The novelty of the proposed method is that it utilizes only the size of packets exchanged between IPs within seconds. By investigating the ratio between the upload and download traffic volume of several P2P applications, a characteristic library is constructed. Then the unknown network traffic can be recognized online using this library. The distinguished features of the proposed method lie in that fast computation, high identification accuracy, and resource-saving capability. Finally, experiment results show the satisfactory performance of the proposed method.