吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (2): 459-464.doi: 10.13229/j.cnki.jdxbgxb201402029

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Online network traffic classification using relevant vector machine

XIA Jing-bo, BAI Jun, ZHAO Xiao-huan, WU Ji-xiang   

  1. Information and Navigation College, Air Force Engineering University, Xi'an 710077, China
  • Received:2012-11-08 Online:2014-02-01 Published:2014-02-01

Abstract:

Based on the research and analysis of probabilistic classification and its influence on the overall accuracy,a new online traffic classification method is proposed. First,the Relevant Vector Machine (RVM) is used to classify traffic flows and output probabilistic classification.Then,the flows, whose classification probability is in doubting interval[0.1,0.9],are re-identified by using port & Deep Packet Inspection (DPI).If the predicted probability is in the interval [0,0.1] and [0.9,1],the classification is totally accepted.Experiment studies demonstrate that the proposed method can reach the overall accuracy of 98%,and perform well in online network traffic classification.

Key words: computer application, traffic classification, relevant vector machine, traffic features, douting interval

CLC Number: 

  • TP393

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