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. © Springer-Verlag Berlin Heidelberg 2009.
Cite
CITATION STYLE
Valenti, S., Rossi, D., Meo, M., Mellia, M., & Bermolen, P. (2009). Accurate, fine-grained classification of P2P-TV applications by simply counting packets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5537 LNCS, pp. 84–92). https://doi.org/10.1007/978-3-642-01645-5_10
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