Generalization performance analysis of flow-based peer-to-peer traffic identification

Yi Hsien Wang, Victor Gau, Trevor Bosaw, Jenq Neng Hwang*, Alan Lippman, Dan Lieberman, I-Chen Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, we develop a peer-to-peer (P2P) traffic identifier to facilitate quality of service (QoS) control in edge routers. Currently, since P2P applications consume a great percentage of Internet bandwidth, certain network optimization strategies are needed to improve the network performance. Traffic identification is the most important component that could be adopted in these optimization strategies. In this paper, we focus on developing a machine learning strategy to perform quick identification, and continuous tracking of flows associated with various P2P media streaming and file sharing applications. With the use of Random Forests (RF) and evaluated by using 10-fold cross validation, our method achieves greater than 98% accuracy rate and 89% precision rate of identifying the P2P flows, with less than 1% false positive rate. With the help of winner-take-all strategy, the generalization performance of using the RF built with data collected from one network to classify flows in other networks can achieve accuracy of being over 97%, with the precision being over 81% and the FP rate being below 2%.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages267-272
Number of pages6
DOIs
StatePublished - 1 Dec 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: 16 Oct 200819 Oct 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Conference

Conference2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period16/10/0819/10/08

Fingerprint Dive into the research topics of 'Generalization performance analysis of flow-based peer-to-peer traffic identification'. Together they form a unique fingerprint.

Cite this