A Classifiable Sub-Flow Selection Method for Traffic Classification in Mobile IP Networks
| Autor: | Toru Abe, Toshiaki Osada, Norio Shiratori, Tetsuo Kinoshita, Akihiro Satoh, Gen Kitagata |
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| Rok vydání: | 2010 |
| Předmět: |
Computer science
business.industry computer.software_genre Machine learning Network management ComputingMethodologies_PATTERNRECOGNITION Traffic classification Mobile IP Traffic engineering Data mining Selection method Artificial intelligence business computer Classifier (UML) Software Information Systems |
| Zdroj: | Journal of Information Processing Systems. 6:307-322 |
| ISSN: | 1976-913X |
| DOI: | 10.3745/jips.2010.6.3.307 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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