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37th Annual IEEE Conference on Local Computer Networks
Applying temporal feedback to rapid identification of BitTorrent traffic
Clearwater Beach, FL, USA USA
October 22-October 25
ISBN: 978-1-4673-1565-4
Jason But, Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia
Philip Branch, Centre for Advanced Internet Architectures, Swinburne University of Technology, Melbourne, Australia
BitTorrent is one of the dominant traffic generating applications in the Internet. The ability to identify BitTorrent traffic in real-time could allow network operators to manage network traffic more effectively. In this paper we demonstrate that erroneous output of a Machine Learning based classifier is randomly distributed within a flow, allowing the application of temporal feedback to improve the overall classifier performance. We propose and evaluate a number of feedback algorithms. Our results show that we are able to improve classification outcomes (Recall by 2.4% and Precision by 0.1%) whilst both improving classification timeliness from three to two minutes, and improving robustness against future changes to the BitTorrent protocol.
Index Terms:
Robustness,Jacobian matrices,Machine learning
Citation:
Jason But, Philip Branch, "Applying temporal feedback to rapid identification of BitTorrent traffic," lcn, pp.204-207, 37th Annual IEEE Conference on Local Computer Networks, 2012
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