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An Empirical Investigation of Filter Attribute Selection Techniques for High-Speed Network Traffic Flow Classification

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Abstract

Attribute selection is an important methodology for data mining problems. Removing irrelevant and redundant attributes from original data set can greatly simplify building classifier models. In this paper, we consider applying attribute selection techniques to network traffic flow classification and conduct experiments using the actual network data collected from the Internet of China. The results show that building with an appropriate attribute selection method can simplify the network traffic classifier while achieving satisfactory classification accuracy.

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Authors and Affiliations

  1. Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Jie Yang, Jing Ma, Yixuan Wang, Lun Yuan & Chao Dong

  2. Microsoft Corporation, Redmond, WA, USA

    Gang Cheng

  3. 10 XiTuCheng Road, HaiDian District, PO Box 151, Beijing, 100876, China

    Jie Yang

Authors
  1. Jie Yang
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  2. Jing Ma
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  3. Gang Cheng
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  4. Yixuan Wang
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  5. Lun Yuan
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  6. Chao Dong
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Correspondence to Jie Yang.

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Yang, J., Ma, J., Cheng, G. et al. An Empirical Investigation of Filter Attribute Selection Techniques for High-Speed Network Traffic Flow Classification. Wireless Pers Commun 66, 541–558 (2012). https://doi.org/10.1007/s11277-012-0735-y

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  • DOI: https://doi.org/10.1007/s11277-012-0735-y

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