TrafficS: a behavior-based network traffic classification benchmark system with traffic sampling functionality

Authors:
Xiaoyan Yan;Bo Liang;Tao Ban;Shanqing Guo;Liming Wang
Affiliations:
School of Computer Science and Technology, Shandong University, China;School of Computer Science and Technology, Shandong University, China;National Institute of Information and Communications Technology, Japan;School of Computer Science and Technology, Shandong University, China;DNSLAB, China Internet Network Information Center, Beijing, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

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Abstract

In recent years, there have been many methods proposed to perform network traffic classification based on application protocols. Still, there is a pressing need for a practical tool to benchmark the performance of these approaches in real-world high-performance network environments. In this paper, based on rigorous requirements analysis on real-world environments, we present a real-time traffic classification benchmark system, termed TrafficS, which aims at easy performance-evaluation between different intelligent methods. TrafficS is not only extensible to incorporate multiple traffic classification engines but supports different packet/stream sampling techniques as well. Furthermore, it could provide users a comprehensive means to perceive the difference between inspected methods in various aspects.