Support Vector Machine Detection of Peer-to-Peer Traffic

Abstract

In this paper, we apply support vector machines to identify peer-to-peer p2p traffic in high-performance routers. 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. The results in this paper suggest that support vector machine detection of p2p traffic in high-performance routers is highly successful and outperforms recent approaches like 1