Rapid identification of Skype traffic flows

Authors:
Philip A. Branch;Amiel Heyde;Grenville J. Armitage
Affiliations:
Swinburne University of Technology, Melbourne, Australia;Swinburne University of Technology, Melbourne, Australia;Centre for Advanced Internet Architectures, Melbourne, Australia
Venue:
Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video
Year:
2009

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Abstract

In this paper we present results of experimental work using machine learning techniques to rapidly identify Skype traffic. We show that Skype traffic can be identified by observing 5 seconds of a Skype traffic flow, with recall and precision better than 98%. We found the most effective features for classification were characteristic packet lengths less than 80 bytes, statistics of packet lengths greater than 80 bytes and inter-packet arrival times. Our classifiers do not rely on observing any particular part of a flow. We also report on the performance of classifiers built using combinations of two of these features and of each feature in isolation.