Statistic Signature based Application Traffic Classification 


Vol. 34,  No. 11, pp. 1234-1244, Nov.  2009


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  Abstract

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services and applications on Internet, which makes the need of application-level traffic classification important for the efficient management and control of network resources. Although lots of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in terms of accuracy and completeness. In this paper we propose an application traffic classification method using statistic signatures, defined as a directional sequence of packet size in a flow, which is unique for each application. The statistic signatures of each application are collected by our automatic grouping and extracting mechanism which is mainly described in this paper. By matching to the statistic signatures we can easily and quickly identify the application name of traffic flows with high accuracy, which is also shown by comprehensive excrement with our campus traffic data.

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  Cite this article

[IEEE Style]

J. Park, S. Yoon, J. Park, S. Lee, M. Kim, "Statistic Signature based Application Traffic Classification," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 11, pp. 1234-1244, 2009. DOI: .

[ACM Style]

Jin-wan Park, Sung-ho Yoon, Jun-sang Park, Sang-woo Lee, and Myung-sup Kim. 2009. Statistic Signature based Application Traffic Classification. The Journal of Korean Institute of Communications and Information Sciences, 34, 11, (2009), 1234-1244. DOI: .

[KICS Style]

Jin-wan Park, Sung-ho Yoon, Jun-sang Park, Sang-woo Lee, Myung-sup Kim, "Statistic Signature based Application Traffic Classification," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 11, pp. 1234-1244, 11. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880