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

Authors:
Francisco J. González-Castaño;Pedro S. Rodríguez-Hernández;Rafael P. Martínez-Álvarez;Andrés Gómez-Tato
Affiliations:
Departamento de Ingeniería Telemática, Universidad de Vigo, Spain, ETSI Telecomunicación, Campus, 36310 Vigo, Spain;Departamento de Ingeniería Telemática, Universidad de Vigo, Spain, ETSI Telecomunicación, Campus, 36310 Vigo, Spain;Departamento de Ingeniería Telemática, Universidad de Vigo, Spain, ETSI Telecomunicación, Campus, 36310 Vigo, Spain;CESGA, Spain
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

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 individualbandwidth 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].