Information Systems Frontiers

, Volume 12, Issue 2, pp 149–156

An SVM-based machine learning method for accurate internet traffic classification

Article

DOI: 10.1007/s10796-008-9131-2

Cite this article as:
Yuan, R., Li, Z., Guan, X. et al. Inf Syst Front (2010) 12: 149. doi:10.1007/s10796-008-9131-2

Abstract

Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% is achieved when un-biased training and testing samples are used with the same feature set. Furthermore, as all the feature parameters are computable from the packet headers, the proposed method is also applicable to encrypted network traffic.

Keywords

Internet trafficNetwork traffic classificationMachine learningFeature selectionSVM

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Center for Intelligent and Networked Systems, TNLIST LabTsinghua UniversityBeijingChina
  2. 2.MOE KLINNS Lab and SKLMS LabXi’an Jiaotong UniversityXi’anChina
  3. 3.College of Economics and ManagementBeijing Jiaotong UniversityBeijingChina
  4. 4.Department of Information Technology and Decision ScienceOld Dominion UniversityNorfolkUSA