Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

It is estimated that 70% or more of broadband bandwidth is consumed by transmitting music, games, video and other content through Peer-to-Peer (P2P) clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a network processors (NPs) based online hybrid traffic classifier is proposed. The designed hardware classifier is able to classify P2P traffic based on the static characteristic namely on line speed, and the Flexible Neural Tree(FNT) based software classifier helps learning and selecting P2P traffic attributes from the statistical characteristics of the P2P traffic. Experiment results illustrate that the hybrid classifier performs well for online classification of P2P traffic from gigabit network. The proposed framework also depicts good expansion capabilities to add new P2P features and to adapt to new P2P applications online.