Akihiro Satoh, Toshiaki Osada, Toru Abe, Gen Kitagata, Norio Shiratori, Tetsuo Kinoshita, Journal of Information Processing Systems Vol. 6, No. 3, pp. 307-322, Sep. 2010  

https://doi.org/10.3745/JIPS.2010.6.3.307
Keywords: Mobile IP Network, Traffic Classification, Network Management, Traffic Engineering, Machine Learning
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Abstract

Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.

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