2010 · 21 citations · 16 references
ComputingComputer ScienceData MiningDigital CommunicationMachine LearningNetwork Performance AnalysisNetwork ProtocolsNetwork Traffic AnalysisPattern RecognitionPeer-to-peer NetworkingPeer-to-peer NetworksApplication Traffic IdentificationGround Truth ConstructionMachine Learning For Traffic AnalysisPacket ClassificationTraffic ClassificationTraffic Feature ExtractionTraffic Monitoring
Abstract—Peer-to-Peer IP Television (P2PTV) applications represent one of the fastest growing application classes on the Internet, both in terms of their popularity and in terms of the amount of traffic they generate. While network operators require monitoring tools that can effectively analyze the traffic produced by these systems, few techniques have been tested on these mostly closed-source, proprietary applications. In this paper we examine the properties of three traffic classifiers applied to the problem of identifying P2PTV traffic. We report on extensive experiments conducted on traffic traces with reliable ground truth information, highlighting the benefits and shortcomings of each approach. The results show that not only their performance in terms of accuracy can vary significantly, but also that their usability features suggest different effective aspects that can be integrated. I.
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Estimating the Support of a High-Dimensional Distribution
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A Measurement Study of a Large-Scale P2P IPTV System
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Traffic classification using clustering algorithms
Jeffrey Erman, Martin Arlitt, Anirban Mahanti · 2006 · 706 citations