DocumentCode :
3126782
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
fYear :
2012
fDate :
22-25 Oct. 2012
Firstpage :
204
Lastpage :
207
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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Local Computer Networks (LCN), 2012 IEEE 37th Conference on
Conference_Location :
Clearwater, FL
ISSN :
0742-1303
Print_ISBN :
978-1-4673-1565-4
Type :
conf
DOI :
10.1109/LCN.2012.6423609
Filename :
6423609
Link To Document :
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