Rapid Identification of BitTorrent traffic

Authors:
But, Jason But;Branch, Philip Branch;Le, Tung Le
Affiliations:
Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia;Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia;Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia
Venue:
LCN '10 Proceedings of the 2010 IEEE 35th Conference on Local Computer Networks
Year:
2010

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Timely and continuous machine-learning-based classification for interactive IP traffic

IEEE/ACM Transactions on Networking (TON)

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Abstract

BitTorrent is one of the dominant traffic generating applications in the Internet today. The ability to identify BitTor-rent traffic in real-time could allow network operators to better manage network traffic and provide a better service to their customers. In this paper we analyse the statistical properties of BitTorrent traffic and select four features that can be used for real-time classification using Machine Learning techniques. We then train and test a classifier using the C4.5 algorithm. Our results show that based on statistics calculated on 150-packet sub-flows, we can classify BitTorrent traffic with Recall of 98.2% and Precision of 96.5%. We then show that 98.1% of sub-flows from other client-server bulk transfer applications are correctly classified as non-BitTorrent.