文章基本信息

标题：Realtime Encrypted Traffic Identification using Machine Learning
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作者：Gu, Chengjie ; Zhang, Shunyi ; Sun, Yanfei 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2011
卷号：6
期号：6
页码：1009-1016
DOI：10.4304/jsw.6.6.1009-1016
语种：English
出版社：Academy Publisher
摘要：Accurate network traffic identification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. The emergence of many new encrypted applications which use dynamic port numbers and masquerading techniques causes the most challenging problem in network traffic identification field. One of the challenging issues for existing traffic identification methods is that they can’t classify online encrypted traffic. To overcome the drawback of the previous identification scheme and to meet the requirements of the encrypted network activities, our work mainly focuses on how to build an online Internet traffic identification based on flow information. We propose realtime encrypted traffic identification based on flow statistical characteristics using machine learning in this paper. We evaluate the effectiveness of our proposed method through the experiments on different real traffic traces. By experiment results and analysis, this method can classify online encrypted network traffic with high accuracy and robustness.
关键词：P2P;machine learning;encrypted traffic;traffic identification