Rapid identification of Skype traffic flows
Philip Branch, Amiel A. Heyde, Grenville J. Armitage · 2009
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.