Abstract—This demo focuses on the online classification of traffic generated by P2P-TV applications, live video delivering services used by an ever increasing number of users worldwide. We designed a novel behavioural technique, which is able to reliably identify P2P-TV traffic simply based on raw counts of packets and bytes exchanged by the application during small-time windows. The demo software aims at showing the classification process and results, allowing users to interact on a simple active testbed with different live running P2P-TV applications. I.

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.