Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets

Silvio Valenti, Dario Rossi, Michela Meo, Marco Mellia, Paola Bermolen

Abstract: 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.