2009 | OriginalPaper | Buchkapitel
Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets
verfasst von : Silvio Valenti, Dario Rossi, Michela Meo, Marco Mellia, Paola Bermolen
Erschienen in: Traffic Monitoring and Analysis
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We present a novel methodology to accurately classify the traffic generated by P2P-TV applications, relying only on the count of packets they exchange with other peers during small time-windows. The rationale is that even a raw count of exchanged packets conveys a wealth of useful information concerning several implementation aspects of a P2P-TV application – such as network discovery and signaling activities, video content distribution and chunk size, etc. By validating our framework, which makes use of Support Vector Machines, on a large set of P2P-TV testbed traces, we show that it is actually possible to reliably discriminate among different applications by simply counting packets.