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Classifying internet applications on early stage in 3G environment

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dc.contributor.authorQuan, Taizhong-
dc.contributor.authorLee, Seongjin-
dc.contributor.authorWon, Youjip-
dc.date.accessioned2024-12-03T04:30:50Z-
dc.date.available2024-12-03T04:30:50Z-
dc.date.issued2009-11-
dc.identifier.issn2374-0272-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/73972-
dc.description.abstractApplication identification plays a crucial role in network management. Among many different approaches introduced in the field, Bernaille et al. [1] introduces connection based clustering to identify applications. In this paper, first four packet sizes with directions are used as a feature set to cluster different applications in wireless environment, which is acclaimed to cluster with 93.7% of accuracy in [1]. However, our experiments not only show that it has low identification accuracy (89.3%), but also show clustering is not suitable for identification of wireless applications.-
dc.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleClassifying internet applications on early stage in 3G environment-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICNIDC.2009.5360910-
dc.identifier.scopusid2-s2.0-77649318692-
dc.identifier.wosid000275138000089-
dc.identifier.bibliographicCitation2009 IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT, PROCEEDINGS, pp 435 - 439-
dc.citation.title2009 IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT, PROCEEDINGS-
dc.citation.startPage435-
dc.citation.endPage439-
dc.type.docTypeProceedings Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Hardware & Architecture-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordAuthorApplication classification-
dc.subject.keywordAuthor3G-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorClustering-
dc.subject.keywordAuthornetwork measurement-
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