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Android恶意应用检测中特征选择算法的研究.docx

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Android恶意应用检测中特征选择算法的研究 Title:ResearchonFeatureSelectionAlgorithmsinAndroidMalwareDetection Abstract: AsthepopularityofAndroiddevicescontinuestogrow,sodoestheriskofmalwareattacksonthesedevices.Androidmalwaredetectionhasemergedasacriticalresearchtopic.Onecrucialaspectofmalwaredetectionistheselectionofrelevantfeaturesthatcanaccuratelydifferentiatebetweenmaliciousandbenignapplications.ThispaperaimstoinvestigatevariousfeatureselectionalgorithmsusedinAndroidmalwaredetection,highlightingtheirstrengths,weaknesses,andpotentialimprovements.ThefindingsofthisresearchcancontributetothedevelopmentofmoreeffectiveandefficientAndroidmalwaredetectionsystems. 1.Introduction: WiththeincreasingadoptionofAndroiddevicesandtheextensiveuseofmobileapplications,thethreatofmaliciousapplicationshasalsorisen.Malwarecancausesignificantdamagetousers'privacy,dataintegrity,andsystemperformance.Thus,theneedforefficientAndroidmalwaredetectiontechniquesbecomesparamount.Featureselectionalgorithmsplayanessentialroleinidentifyingandextractingrelevantfeaturesfromapplicationdatasetstodistinguishbetweenmaliciousandbenignappsaccurately. 2.TheImportanceofFeatureSelectioninAndroidMalwareDetection: Featureselectioniscrucialtoreducethedimensionalityofthefeaturespaceandimprovetheperformanceofmalwaredetectionmodels.Byselectingasubsetoffeatures,wecaneliminatenoise,irrelevant,orredundantfeatures,thusenhancingtheoverallefficiencyandeffectivenessoftheclassificationalgorithm.Additionally,featureselectionhelpsmitigatetheoverfittingproblembyreducingthecomplexityofthemodel. 3.CommonlyUsedFeatureSelectionAlgorithmsinAndroidMalwareDetection: ThissectionprovidesanoverviewofseveralfeatureselectionalgorithmscommonlyusedinAndroidmalwaredetection.Thealgorithmsincludebutarenotlimitedto: -InformationGain(IG):IGmeasurestherelevanceofafeaturebyassessingitsentropyreductioncapability. -Chi-Square(χ2):χ2measuresthedependencebetweenfeaturesandclasslabels,highlightingthemostdiscriminatoryfeatures. -MutualInformation(MI):MImeasurestheamou