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Impact of Asymmetry of Internet Traffic for Heuristic Based Classification
Chris Richter, Michael Finsterbusch, Klaus HanBgen, Jean-Alexander Muller
Pages - 167 - 176     |    Revised - 15-11-2012     |    Published - 31-12-2012
Volume - 4   Issue - 5    |    Publication Date - December 2012  Table of Contents
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KEYWORDS
Flow Classification, Internet Traffic, Traffic Identification
ABSTRACT
Accurate traffic classification is necessary for many administrative networking tasks like security monitoring, providing Quality of Service and network design or planning. In this paper we illustrate the accuracy of 18 different machine learning algorithms with different statistical parameter combinations. Additionally, we divide the statistical parameters into upstream and downstream to observe the influence of the protocol inherent differences of client and server behaviour for traffic classification. Our results show that this differentiation can increase the protocol detection rate and decrement the processing time.
CITED BY (1)  
1 Finsterbusch, M., & Muller, J. A. (2013, October). Formal methods to improve the identification and validation of network traffic. In Network Protocols (ICNP), 2013 21st IEEE International Conference on (pp. 1-3). IEEE.
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Mr. Chris Richter
HTWK Leipzig - Germany
richter@hftl.de
Mr. Michael Finsterbusch
HTWK Leipzig - Germany
Mr. Klaus HanBgen
- Germany
Mr. Jean-Alexander Muller
- Germany