Recent research on evaluation and comparison of traffic classification systems only used tagged offline dataset, thus the result can only reflect the performance of the classification systems on the network from which the offline dataset was collected. Besides, the difference of scopes and granularities of different traffic classification systems also render them not comparable. In this work, we propose a novel two-phased evaluation system which combines offline dataset evaluation and online evaluation. Our evaluation approach can help network manager pick the traffic classification system that fit their specific network most. In addition, we introduce three metrics corresponding to our evaluation scheme to do comprehensive evaluation and group applications according to their behaviors and functions to compare classification systems of different granularities.

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Authors and Affiliations

1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

   Yong Zhao, Yao Yao & Gang Xiong

2. National Computer Network Emergency Response Technical Team, Coordination Center of China, China

   Yuan Yuan & Yong Wang

Editors and Affiliations

1. School of Computer Science, National University of Defense Technology, 410073, Changsha, China

   Weihong Han

2. School of Information Technology & Electrical Engineering, University of Queensland, 4107, Brisbane, QLD, Australia

   Zi Huang

3. School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China

   Changjun Hu

4. School of Computer Science and Technology, Harbin Institute of Technology, 150006, Harbin, China

   Hongli Zhang

5. Institute of Information Engineering, Chinese Academy of Sciences, 100864, Beijing, China

   Li Guo

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