JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (09): 109-114.doi: 10.6040/j.issn.1671-9352.2.2014.106

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An efficient network traffic classification scheme based on similarity

DU Rui-ying1, YANG Yong2, CHEN Jing1, WANG Chi-heng1   

  1. 1. School of Computer, Wuhan University, Wuhan 430072, Hubei, China;
    2. International School of Software, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2014-06-24 Revised:2014-08-27 Online:2014-09-20 Published:2014-09-30

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Abstract: Support Vector Machine is a classification algorithm that combines high efficiency, high accuracy and real time. There's a problem when SVM makes its decision for an un-labeled instance because uninvolved classifiersparticipate in that affects SVM's real time performance and reliability. Thus, a method utilized Efficient SVM based on Similarity (ESVMS) for traffic classification was proposed. ESVMS estimates the classes that an un-labeled instances may belongs to as to kick out the uninvolved classifiers. Experimental results show that ESVMS holds the accuracy of SVM's and improves its real time performance.

Key words: machine learning, support vector machine, network traffic classification

CLC Number: 

  • TP393
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