Fine-grained P2P traffic classification by simply counting flows

The continuous emerging of peer-to-peer(P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2 P applications monitoring, in particular,P2 P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2 P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2 P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2 P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.