Support Vector Machine Detection of Peer-to-Peer Traffic in High-Performance Routers with Packet Sampling

摘要

In this paper, we explore the possibilities of support vector machines to identify peer-to-peer (p2p) traffic in high-performance routers with packet sampling. Commercial networks limit user access bandwidth -either physically or logically-. However, in research networks there are no individual bandwidth restrictions, since this would interfere with research tasks. User behavior in research networks has changed radically with the advent of p2p multimedia file transfers: many users take advantage of the huge bandwidth (e.g. compared to domestic DSL access) to exchange movies and the like. This behavior may have a deep impact on research network utilization. Consequently, in the framework of the MOLDEIP project, we have proposed to apply support vector machine detection to identify those activities in high-performance research network routers. Due to their high port rates, those routers cannot extract the headers of all the packets that traverse them, but only a sample. The results in this paper suggest that support vector machine detection of p2p traffic in high-performance routers with packet sampling is highly successful and outperforms recent approaches like [1].