Volume 6 Number 6 (Jun. 2011)
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JSW 2011 Vol.6(6): 1009-1016 ISSN: 1796-217X
doi: 10.4304/jsw.6.6.1009-1016

Real-time Encrypted Traffic Identification using Machine Learning

Chengjie Gu, Shunyi Zhang, Yanfei Sun

Institute of Information Networks Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

Abstract—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 real-time 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.

Index Terms—P2P, machine learning, encrypted traffic, traffic identification

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Cite: Chengjie Gu, Shunyi Zhang, Yanfei Sun, "Real-time Encrypted Traffic Identification using Machine Learning," Journal of Software vol. 6, no. 6, pp. 1009-1016, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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