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一种改进XGboost的DoH流量分类方法
Title:EnhancedXGBoostApproachforDNSoverHTTPS(DoH)TrafficClassification
Abstract:
TheincreasingadoptionofDNSoverHTTPS(DoH)hasintroducednewchallengesfornetworktrafficclassification.ThispaperpresentsanenhancedXGBoostapproachtoaddressthesechallengesandimprovetheaccuracyofDoHtrafficclassification.WeproposeanovelfeatureengineeringtechniqueandleveragetheadvancedcapabilitiesofXGBoosttoovercomelimitationsinexistingmethods.Experimentalresultsdemonstratetheeffectivenessofourapproach,showcasingitspotentialforaccurateDoHtrafficclassification.
1.Introduction
DNSoverHTTPS(DoH)isaprotocolthatencryptsDNSqueriesusingHTTPS,providingimprovedprivacyandsecurity.However,italsointroduceschallengesfortraditionalnetworktrafficclassificationtechniques,asitmasksthepayloadcontents.AccurateidentificationofDoHtrafficiscrucialfornetworkmanagement,securityanalysis,andmonitoringpurposes.ThispaperaimstoenhancetheclassificationofDoHtrafficusingtheXGBoostalgorithm.
2.RelatedWork
ThissectiondiscussesexistingapproachesforDoHtrafficclassification.CurrentmethodologiesmainlyrelyonstatisticalandmachinelearningtechniquessuchasnaiveBayes,decisiontrees,andrandomforests.However,thesemethodsoftenstruggletoaccuratelyclassifyDoHtrafficduetotheencryptednatureoftheprotocol.
3.Proposals
Inthissection,wepresentourenhancedXGBoostapproachforDoHtrafficclassification.Themaincontributionsareasfollows:
a)Novelfeatureengineering:WeproposeasetofnewfeaturesthatcapturespecificpatternsandcharacteristicsofDoHtraffic.Thesefeaturesincludepacketsizestatistics,flowfeatures,andfrequency-basedfeatures.ByincorporatingthesefeaturesintotheXGBoostmodel,weaimtoenhancetheaccuracyofDoHtrafficclassification.
b)XGBoostoptimization:WeexploittheadvancedcapabilitiesoftheXGBoostalgorithm,includinggradientboosting,regularization,andensemblelearning.ThesetechniqueshelpinhandlingtheinherentnoiseandimbalanceinDoHtrafficdata,furtherimprovingtheaccuracyofclassification.
4.ExperimentalSetup
Toevaluatetheeffectivenessofourproposedappro