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

*此作品的通信作者

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

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%.

原文English
主出版物標題Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
頁面267-272
頁數6
DOIs
出版狀態Published - 2008
事件2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
持續時間: 16 10月 200819 10月 2008

出版系列

名字Proceedings 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
國家/地區Mexico
城市Cancun
期間16/10/0819/10/08

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