Abacus: Accurate behavioral classification of P2P-TV traffic
Abstract Peer-to-Peer streaming (P2P-TV) applications offer the capability to watch real time video over the Internet at low cost. Some applications have started to become popular, raising the concern of Network Operators that fear the large amount of traffic they might generate. Unfortunately, most of P2P-TV applications are based on proprietary and unknown protocols, and this makes the detection of such traffic challenging per se. In this paper, we propose a novel methodology to accurately classify P2P-TV traffic and to identify the specific P2P-TV application which generated it. Our proposal relies only on the count of packets and bytes exchanged among peers during small time-windows: the rationale is that these two counts convey a wealth of useful information, concerning several aspects of the application and its inner workings, such as signaling activities and video chunk size. Our classification framework, which uses Support Vector Machines, accurately identifies P2P-TV traffic as well as traffic that is generated by other kinds of applications, so that the number of false classification events is negligible. By means of a large experimental campaign, which uses both testbed and real network traffic, we show that it is actually possible to reliably discriminate between different P2P-TV applications by simply counting packets.