Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines

Authors:
Rui Wang;Yang Liu;Yuexiang Yang;Xiaoyong Zhou
Affiliations:
National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China;National University of Defense Technology, China
Venue:
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Year:
2006

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Abstract

Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portion of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. App-level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and app-level classification, which merely uses transport layer information. The method uses optimized Support Vector Machines to perform large learning tasks, which is common in network traffic identification. The experimental results show that the proposed method has high efficiency and promising accuracy.