基于多特征的Android移动设备恶意代码检测-豆柴文库

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基于多特征的Android移动设备恶意代码检测
Title:Multi-FeatureBasedAndroidMalwareDetectiononMobileDevices
Abstract:
WiththerapidgrowthoftheAndroidplatformandtheincreasingnumberofmobiledevicesconnectedtotheinternet,thethreatofmalicioussoftwaretargetingthesedeviceshasbecomeasignificantconcern.Thispaperpresentsamulti-featurebasedapproachfordetectingmaliciouscodeonAndroidmobiledevices.Theproposedsolutioncombinesseveralfeatures,includingsystemcallsequences,permissionrequests,APIcalls,andcodepatterns,toaccuratelyidentifymalwarewhileminimizingfalsepositives.ExperimentalresultsdemonstratetheeffectivenessoftheapproachindetectingandclassifyingmaliciousAndroidapplications.
1.Introduction
Mobiledeviceshavebecomeanintegralpartofourdailylives,providingconvenience,communication,andaccesstovastamountsofinformation.However,theopennatureoftheAndroidplatformhasmadeitanattractivetargetforcybercriminalsdevelopingmaliciousapplications.Therefore,theneedforefficientandeffectivemalwaredetectiontechniquesforAndroiddevicesiscrucial.
2.RelatedWork
ThissectionprovidesanoverviewofexistingapproachesforAndroidmalwaredetection,focusingonthestrengthsandlimitationsofeachmethod.Ithighlightstheneedforamulti-featurebasedapproachtoenhancedetectionaccuracy.
3.Methodology
Theproposedmulti-featurebasedapproachcombinesseveralfeaturesextractedfromAndroidapplications,leveragingbothstaticanddynamicanalysistechniques.Systemcallsequencesarecapturedduringtheruntimeoftheapplication,providingvaluableinformationaboutthebehaviorofthecode.PermissionrequestsandAPIcallsarealsoconsidered,astheycanbeindicativeofsuspiciousactivities.Furthermore,codepatternanalysisisutilizedtoidentifyknownmaliciouspatternsintheapplication'scode.
4.FeatureExtraction
ThissectiondetailstheprocessofextractingfeaturesfromAndroidapplicationsforsubsequentanalysis.Itdiscussesthetechniquesusedtoextractsystemcallsequences,permissionrequests,APIcalls,andcodepatterns.
5.MalwareDetectionModel
TheextractedfeaturesarethenusedtotrainamachinelearningmodelforclassifyingAndroida