doi: 10.4304/jsw.6.6.1009-1016
Real-time Encrypted Traffic Identification using Machine Learning
- 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.
Keywords
- P2P
- machine learning
- encrypted traffic
How to Cite
Chengjie Gu, Shunyi Zhang, and Yanfei Sun, "Real-time Encrypted Traffic Identification using Machine Learning," Journal of Software, vol. 6, no. 6, pp. 1009-1016, 2011. https://doi.org/ 10.4304/jsw.6.6.1009-1016
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).