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Mobile Information Systems
Volume 2016 (2016), Article ID 6180527, 13 pages
http://dx.doi.org/10.1155/2016/6180527
Research Article

High-Performance Internet Traffic Classification Using a Markov Model and Kullback-Leibler Divergence

1Department of Statistics, Inha University, Incheon, Republic of Korea
2School of Information and Communication Engineering, Inha University, Incheon, Republic of Korea

Received 6 August 2015; Revised 9 November 2015; Accepted 14 December 2015

Academic Editor: Francesco Palmieri

Copyright © 2016 Jeankyung Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As internet traffic rapidly increases, fast and accurate network classification is becoming essential for high quality of service control and early detection of network traffic abnormalities. Machine learning techniques based on statistical features of packet flows have recently become popular for network classification partly because of the limitations of traditional port- and payload-based methods. In this paper, we propose a Markov model-based network classification with a Kullback-Leibler divergence criterion. Our study is mainly focused on hard-to-classify (or overlapping) traffic patterns of network applications, which current techniques have difficulty dealing with. The results of simulations conducted using our proposed method indicate that the overall accuracy reaches around 90% with a reasonable group size of .