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一种在Spark框架下的基于改进随机森林的Android恶意软件检测方法.docx

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一种在Spark框架下的基于改进随机森林的Android恶意软件检测方法 Title:AnImprovedRandomForest-BasedAndroidMalwareDetectionMethodintheSparkFramework Abstract: TheriseinthenumberofAndroidmalwareposesasignificantthreattouserprivacyandsecurity.Traditionalmalwaredetectionmethodsoftenstruggletokeepupwiththeincreasingsophisticationofmalware.Inthispaper,weproposeanimprovedrandomforest-basedAndroidmalwaredetectionmethodintheSparkframework.ByleveragingthedistributedcomputingcapabilitiesofSpark,ourapproachaimstoenhancethespeedandaccuracyofmalwaredetectionontheAndroidplatform. 1.Introduction: WiththerapidgrowthoftheAndroidplatform,thenumberofmalwarespecificallytargetingAndroiddeviceshasalsoincreaseddramatically.MalwarecreatorstakeadvantageoftheopennessoftheAndroidecosystemtoexploitvulnerabilitiesformaliciouspurposes.TraditionalAndroidmalwaredetectionmethods,suchassignature-baseddetectionandpermissionanalysis,havelimitationsinaccuratelyidentifyingnewandunknownmalware.Therefore,thereisaneedformoresophisticatedtechniquestodetectandmitigatetheimpactofAndroidmalware. 2.Background: 2.1AndroidMalware:ThissectionprovidesanoverviewofAndroidmalware,itstypes,andtheconsequencesofmalwareinfectionsonuserprivacyandsecurity. 2.2RandomForest:Weexplaintheconceptofrandomforests,whichareensemblelearningalgorithmswidelyusedforclassificationtasks.Randomforestshaveshownpromisingresultsinvariousdomainsduetotheirabilitytohandlelargedatasetsandhandlethecurseofdimensionality. 3.ProposedMethodology: 3.1DatasetPreparation:WedescribetheprocessofcollectingandpreparingalabeleddatasetofbenignandmaliciousAndroidapplications.Thedatasetisutilizedfortrainingandevaluatingtherandomforest-basedclassificationmodel. 3.2FeatureExtraction:WeidentifyasetofeffectivefeaturesfromtheAndroidapplicationdataset.Thesefeaturescaptureimportantcharacteristicsofmalware,includingpermissions,APIcalls,intentfilters,andmore. 3.3FeatureSelection:Toreducethedimensionalityofthefeaturespaceandimprovetheclassificationaccuracy,weemployfeatureselectiontechniquessuchasinfo