A Four-Stage Hybrid Feature Subset Selection Approach for Network Traffic Classification Based on Full Coverage

  • Jingbo XiaEmail author
  • Jian Shen
  • Yaoxiang Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)


There is significant interest in network management and security to classify traffic flows. As the essential step for machine learning based traffic classification, feature subset selection is often used to realize dimension reduction and redundant information decrease. A four-stage hybrid feature subset selection method is proposed to improve the classification performance of hybrid methods at low evaluation consumption. The proposed algorithm is designed to dispose features in the level of block and evaluate every feature even the remaining ones which cannot provide much information by themselves to use the interactions among all of them. Additionally, a wrapper-based selection is designed in the last stage to further remove the redundant features. The performances are examined by two groups of experiments. Our theoretical analysis and experimental observations reveal that the proposed method selects feature subset with improved classification performance on every index while depleting fewer evaluations. Moreover, the evaluation consumption can keep at a low and stable level with different size of block.


Full coverage Machine learning Hybrid feature subset selection Network traffic classification Network management 



The authors gratefully acknowledge the financial support from Natural Science Foundation of Zhangzhou, Fujian (Project No. ZZ2018J22).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tan Kah CollegeXiamen UniversityZhangzhouChina
  2. 2.Unit 95655 of PLAChengduChina

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