吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 124-127.

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Internet traffic identification by using improved one class support vector machines

WU Qi, LIU Jian-nan, KOU Wen-long, ZHANG Zong-sheng   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2012-08-12 Published:2013-06-01

Abstract:

The theory of One Class Support Vector Machine (OCSVM) has an advantage over limited sample,high-dimensional space and unbalanced datasets.OCSVM parameter selection algorithm was improved by using a weight value simulated annealing method and dynamic inertia factor particle swam algorithm,as a result,the traffic classification accuracy is improved by nearly ten percent.Drawbacks such as low accuracy of the traditional traffic classification and overhead are solved.It is of great significance to improve the quality of network services, network management and control network security and so on.

Key words: traffic classification, machine learning, support vector machine, parameter selection

CLC Number: 

  • TP391

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