Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2011, Vol. 34 ›› Issue (1): 103-106.doi: 10.13190/jbupt.201101.103.zhangj

• Reports • Previous Articles     Next Articles

Network Traffic Identification Based on Online Clustering

  

  • Received:2010-03-09 Revised:2010-08-21 Online:2011-02-28 Published:2011-02-28
  • Contact: Jian ZHANG E-mail:zhangj9860@bupt.edu.cn

Abstract:

To solve the problem of network traffic identification online, a clustering algorithm and a traffic identification scheme is proposed. The scheme uses a few number of the initial data packets in the flows as a subflow, extracts the statistical features from subflows, and extracts the best feature subset of subflows by applying correlationbased filter approach. The network traffic flows are clustered by online density based spatial clustering of applications with noise algorithm, and mapped to application types by the dominant application in clusters. Experiments show that the scheme can identify new application types and encrypted flows, and can be implemented in online network traffic classification.

Key words: traffic identification, online clustering algorithm, feature selection

CLC Number: