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基于流量特征建模的网络异常行为检测技术.docx

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基于流量特征建模的网络异常行为检测技术 Title:NetworkAnomalyDetectionTechniquesBasedonTrafficFeatureModeling Abstract: Withtherapiddevelopmentofnetworktechnology,ensuringthesecurityandstabilityofcomputernetworkshasbecomeacriticaltask.Networkanomalydetectionplaysapivotalroleindetectingandpreventingmaliciousactivities,suchasintrusionattempts,denial-of-serviceattacks,andmalwareinfections.Thispaperfocusesonnetworkanomalydetectiontechniquesbasedontrafficfeaturemodeling.Variousmethodsareexplored,includingstatisticalanalysis,machinelearning,anddeeplearning,toeffectivelyidentifyabnormalnetworkbehaviors.Experimentalresultsdemonstratetheefficacyofthesetechniquesinreal-worldscenarios. 1.Introduction Networkanomalydetectionaimstoidentifydeviationsfromnormalnetworkbehavior,enablingtimelyresponsestopotentialsecuritythreats.Traditionalsignature-basedmethodsareeffectiveforknownattacks,buttheyfailtodetectzero-dayattacksandnovelanomalies.Thus,trafficfeaturemodelinghasemergedasanalternativeapproachthatfocusesonextractingandanalyzingnetworktrafficpropertiestoidentifyabnormalbehaviors. 2.StatisticalAnalysis-BasedTechniques Statisticalanalysistechniquesemploymathematicalandstatisticalmodelstoanalyzenetworktrafficpatterns.Theyareparticularlyusefulfordetectingsimpleanomalies,suchassuddenspikesordropsintrafficvolume,unusualpacketsizes,andabnormalportusage.Thissectiondiscussescommonlyusedstatisticalanalysistechniques,suchasmovingaverage,standarddeviation,andanomalyscorecomputation. 3.MachineLearning-BasedTechniques Machinelearningtechniquesleveragehistoricaltrafficdatatobuildmodelsthatcanclassifynetworkbehaviorsasnormalorabnormal.Thesetechniquesarecapableofdetectingcomplexandpreviouslyunseenanomalies.Commonlyusedmachinelearningalgorithms,includingdecisiontrees,randomforests,supportvectormachines,andneuralnetworks,aredescribedinthissection.Theselectionofappropriatefeatures,featureengineering,andmodelevaluationmetricsarealsodiscussed. 4.DeepLearning-BasedTechniques Deeplearningmethods,specificallydeepneuralnetwor