• DocumentCode
    1838710
  • Title

    A P2P Network Traffic Classification Method Using SVM

  • Author

    Yang, Ai-Min ; Jiang, Sheng-Yi ; Deng, He

  • Author_Institution
    Sch. of Inf., Guangdong Univ. of Foreign Studies, Guangzhou
  • fYear
    2008
  • fDate
    18-21 Nov. 2008
  • Firstpage
    398
  • Lastpage
    403
  • Abstract
    Accurate identification and classification of network traffic according to application type is an important element of many network management tasks. In this paper, a P2P network traffic classification method using SVM classifier is proposed. By this method, the P2P network traffic can been classified according to application types with statistical characteristics of network traffic. This paper mainly introduces the network traffic classification problem on four application types of P2P, namely, BitTorrent, PPLive, Skype, MSN. The classification framework based on the SVM is introduced. The methods to gain the traffic samples and construct classifier are described also. The experimental results show the validity of the proposed methods and the average classification precise rate was high.
  • Keywords
    computer network management; pattern classification; peer-to-peer computing; statistical analysis; support vector machines; telecommunication traffic; BitTorrent; MSN; P2P network traffic classification method; PPLive; Skype; network management tasks; statistical characteristics; support vector machine; Clustering algorithms; Computer networks; Engineering management; Financial management; Machine learning algorithms; Protocols; Support vector machine classification; Support vector machines; Telecommunication traffic; Traffic control; P2P; SVM; network traffic classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
  • Conference_Location
    Hunan
  • Print_ISBN
    978-0-7695-3398-8
  • Electronic_ISBN
    978-0-7695-3398-8
  • Type

    conf

  • DOI
    10.1109/ICYCS.2008.247
  • Filename
    4709006