Title :
Applying temporal feedback to rapid identification of BitTorrent traffic
Author :
But, Jason ; Branch, P.
Author_Institution :
Centre for Adv. Internet Archit., Swinburne Univ. of Technol., Melbourne, VIC, Australia
Abstract :
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.
Keywords :
learning (artificial intelligence); peer-to-peer computing; protocols; telecommunication traffic; BitTorrent protocol; BitTorrent traffic rapid identification; classifier performance; erroneous output; machine learning based classifier; network operator; temporal feedback; Jacobian matrices; Machine learning; Robustness;
Conference_Titel :
Local Computer Networks (LCN), 2012 IEEE 37th Conference on
Conference_Location :
Clearwater, FL
Print_ISBN :
978-1-4673-1565-4
DOI :
10.1109/LCN.2012.6423609