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1、IEEETRANSACTIONSONNETWORKANDSERVICEMANAGEMENT,VOL.10,NO.2,JUNE2013133AnEffectiveNetworkTrafÞcClassiÞcationMethodwithUnknownFlowDetectionJunZhang,Member,IEEE,ChaoChen,StudentMember,IEEE,YangXiang,SeniorMember,IEEE,WanleiZhou,SeniorMember,IEEE,andAthanasiosV.Va
2、silakosSeniorMember,IEEEAbstractTrafficclassificationtechniqueisanessentialtoolßowlevelstatisticalproperties[1],[10].Substantialattentionfornetworkandsystemsecurityinthecomplexenvironmentshasbeenpaidontheapplicationofmachinelearningtech-suchascloudcomputingbase
3、denvironment.Thestate-of-the-niquestoßowstatisticalfeaturesbasedtrafÞcclassiÞcationarttrafficclassificationmethodsaimtotaketheadvantages[2].However,theperformanceoftheexistingßowstatisticalofflowstatisticalfeaturesandmachinelearningtechniques,howevertheclassifica
4、tionperformanceisseverelyaffectedbyfeaturebasedtrafÞcclassiÞcationisstillunsatisÞedinreallimitedsupervisedinformationandunknownapplications.Toworldenvironments.achieveeffectivenetworktrafficclassification,weproposeaAnumberofsupervisedclassiÞcationalgorithmsandu
5、n-newmethodtotackletheproblemofunknownapplicationssupervisedclusteringalgorithmshavebeenappliedtonet-inthecrucialsituationofasmallsupervisedtrainingset.TheproposedmethodpossessesthesuperiorcapabilityofdetectingworktrafÞcclassiÞcation.InsupervisedtrafÞcclassiÞ
6、cationunknownflowsgeneratedbyunknownapplicationsandutilizing[10],[4],[11],[12],[13],[14],[15],theßowclassiÞcationthecorrelationinformationamongreal-worldnetworktrafficmodelislearnedfromthelabelledtrainingsamplesofeachtoboosttheclassificationperformance.Atheoreti
7、calanalysispredeÞnedtrafÞcclass.ThesupervisedmethodsclassifyanyisprovidedtoconfirmperformancebenefitoftheproposedßowsintopredeÞnedtrafÞcclasses,sotheycannotdealwithmethod.Moreover,thecomprehensiveperformanceevaluationconductedontworeal-worldnetworktrafficdataset
8、sshowsthatunknownßowsgeneratedbyunknownapplications.More-theproposedschemeoutperformstheexistingmethodsintheover,toachievehighclassiÞcationaccuracy,thesupervisedcriticalnetworkenvironment
