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Active learning for P2P traffic identification

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Abstract

P2P traffic identification methods by using machine learning have been provided in a great number of works, which suffer from a large and representative labeled sample set. To overcome the sample labeling problem, a new P2P traffic identification approach by active learning called P2PTIAL is presented. P2PTIAL is composed of two parts: support vector machine as learner and uncertainty selection based on distance. In order to improve the effectiveness of P2PTIAL, we add filtering policy and balanced policy to P2PTIAL. Firstly, we use support vector data description (SVDD) theory to filter some unlabeled samples, which have little contribution on active learning, and so it can save computation cost and storage space. Secondly, we use the unlabeled sample’s pre-labeled information to develop balanced policy, which can keep balanced learning. Lastly, we support our design with extensive simulation experiments, and our results show P2PTIAL is feasible.

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Acknowledgments

The authors would like to thank the anonymous referees for their very valuable comments. This work was supported in part by the National Science Foundation of China under (Grant 60973140, 61170276, 61300170, 61373135, 71371012), the Key Project for Outstanding Young Talents in Higher Education Institutions of Anhui Province of China under Grant 2013 SQRL034ZD, the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China under Grant 12 KJA520003, and the Innovation Fund for Technology based Enterprise of Jiangsu Province of China under Grant BC2013027.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Peoples Republic of China

    San-Min Liu & Zhi-Xin Sun

  2. College of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, Peoples Republic of China

    San-Min Liu

  3. Key Laboratory of Broadband Wireless Communication and Sensor Network technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, 210003, Peoples Republic of China

    Zhi-Xin Sun

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  1. San-Min Liu
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  2. Zhi-Xin Sun
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Correspondence to San-Min Liu.

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Liu, SM., Sun, ZX. Active learning for P2P traffic identification. Peer-to-Peer Netw. Appl. 8, 733–740 (2015). https://doi.org/10.1007/s12083-014-0281-3

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  • DOI: https://doi.org/10.1007/s12083-014-0281-3

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