Semi-supervised internet network traffic classification using a Gaussian mixture model

Feng Qian

doi:10.1016/j.aeue.2007.07.006

Semi-supervised internet network traffic classification using a Gaussian mixture model

2008-08-01

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DOI：10.1016/j.aeue.2007.07.006

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With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different internet applications. The major contributions are: (1) we propose a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications; (2) we achieve an optimum configuration for the GMM-based semi-supervised classification system. The effectiveness of these proposed approaches is demonstrated through experimental results. (C) 2007 Elsevier GmbH. All rights reserved.

Keywords:

internet traffic/flows classification

semi-supervised classification

Gaussian mixture model (GMM)

optimum configuration

Semi-supervised internet network traffic classification using a Gaussian mixture model

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