Section: New Results
Classification of Content and Users in BitTorrent by Semi-supervised Learning Methods
P2P downloads still represent a large portion of today?s Internet traffic. More than 100 million users operate BitTorrent and generate more than 30% of the total Internet traffic. Recently, a significant research effort has been done to develop tools for automatic classification of Internet traffic by application. The purpose of the present work is to provide a framework for sub-classification of P2P traffic generated by the BitTorrent protocol. The general intuition is that the users with similar interests download similar contents. This intuition can be rigorously formalised with the help of graph based semi- supervised learning approach. We have chosen to work with PageRank based semi-supervised learning method, which scales well with very large volumes of data. We provide recommendations for the choice of parameters in the PageRank based semi-supervised learning method. In particular, we show that it is advantageous to choose labelled points with large PageRank score.
This work was awarded best paper at the 3rd International Workshop on Traffic Analysis and Classification (in conjunction with the 8th International Wireless Communications and Mobile Computing Conference, 2012)  and led to a companion paper  .