Rapid Identification of BitTorrent traffic
- 1 October 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 536-543
- https://doi.org/10.1109/lcn.2010.5735770
Abstract
BitTorrent is one of the dominant traffic generating applications in the Internet today. The ability to identify BitTorrent 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.Keywords
This publication has 9 references indexed in Scilit:
- Application classification using packet size distribution and port associationJournal of Network and Computer Applications, 2009
- Rapid identification of Skype traffic flowsPublished by Association for Computing Machinery (ACM) ,2009
- Outsourcing automated QoS control of home routers for a better online game experienceIEEE Communications Magazine, 2008
- A Survey of Techniques for Internet Traffic Classification using Machine LearningIEEE Communications Surveys & Tutorials, 2007
- Performance analysis of the ANGEL system for automated control of game traffic prioritisationPublished by Association for Computing Machinery (ACM) ,2007
- A Peer-To-Peer Traffic Identification Method Using Machine LearningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networksProceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006
- Traffic classification on the flyACM SIGCOMM Computer Communication Review, 2006
- Automated traffic classification and application identification using machine learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005