Acta Metallurgica Sinica(English letters)

• Networks • 上一篇    下一篇

Research of the traffic characteristics for the real time online traffic classification

孙美凤,陈经涛   

  1. College of Information Engineering, Yangzhou University, Yangzhou 225009, China
  • 收稿日期:2010-10-15 修回日期:2011-02-21 出版日期:2011-06-30 发布日期:2011-06-13
  • 通讯作者: 孙美凤 E-mail:mfsun@yzu.edu.cn
  • 基金资助:

    国家自然科学基金资助项目

Research of the traffic characteristics for the real time online traffic classification

Meifeng SUN1,Jingtao CHEN2   

  1. College of Information Engineering, Yangzhou University, Yangzhou 225009, China
  • Received:2010-10-15 Revised:2011-02-21 Online:2011-06-30 Published:2011-06-13
  • Contact: Meifeng SUN E-mail:mfsun@yzu.edu.cn

摘要:

Aiming at the hysteretic characteristics of classification problem existed in current internet traffic identification field, this paper investigates the traffic characteristic suitable for the on-line traffic classification, such as quality of service (QoS). By the theoretical analysis and the experimental observation, two characteristics (the ACK-Len ab and ACK-Len ba) were obtained. They are the data volume which first be sent by the communication parties continuously. For these two characteristics only depend on data’s total length of the first few packets on the flow, network traffic can be classified in the early time when the flow arrived. The experiment based on decision tree C4.5 algorithm, with above 97% accuracy. The result indicated that the characteristics proposed can commendably reflect behavior patterns of the network application, although they are simple.

关键词:

on-line traffic classification, traffic characteristics, ACK-Len ab, ACK-Len ba

Abstract:

Aiming at the hysteretic characteristics of classification problem existed in current internet traffic identification field, this paper investigates the traffic characteristic suitable for the on-line traffic classification, such as quality of service (QoS). By the theoretical analysis and the experimental observation, two characteristics (the ACK-Len ab and ACK-Len ba) were obtained. They are the data volume which first be sent by the communication parties continuously. For these two characteristics only depend on data’s total length of the first few packets on the flow, network traffic can be classified in the early time when the flow arrived. The experiment based on decision tree C4.5 algorithm, with above 97% accuracy. The result indicated that the characteristics proposed can commendably reflect behavior patterns of the network application, although they are simple.

Key words:

on-line traffic classification, traffic characteristics, ACK-Len ab, ACK-Len ba