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基于AndroidAPI调用的恶意软件行为检测方法研究.docx

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基于AndroidAPI调用的恶意软件行为检测方法研究 Title:ResearchonMethodsforDetectingMaliciousBehaviorinAndroidAppsUsingAndroidAPIs Abstract: Withtheincreasingpopularityofsmartphonesandthewidespreaduseofmobileapplications,thethreatofmalicioussoftwaretargetingAndroiddeviceshasrisensignificantly.ThisresearchpaperaimstoinvestigateandproposeeffectivemethodsfordetectingmaliciousbehaviorinAndroidappsbyutilizingAndroidAPIs. Introduction: TheAndroidoperatingsystemprovidesarichsetofAPIsthatenabledeveloperstocreatepowerfulandfeature-richapplications.However,theseAPIscanalsobemisusedorexploitedbymaliciousactorstocarryoutharmfulactivitiessuchasdatatheft,privilegeescalation,andunauthorizedaccess. Objective: TheprimaryobjectiveofthisresearchistoexploredifferenttechniquesfordetectingandidentifyingmaliciousbehaviorinAndroidappsbasedontheanalysisofAndroidAPIcalls.ByexaminingtheAPIcallsmadebytheappduringexecution,wecanuncoverpotentialsecurityvulnerabilitiesandsuspectbehaviorsthatindicatemaliciousintent. Methods: 1.StaticAnalysis:Inthismethod,theapp'scodeisexaminedwithoutactuallyexecutingit.TheAPIcallswithinthecodeareanalyzedtoidentifysuspiciouspatternsorbehaviors.ThiscouldincludecheckingforcallstosensitiveAPIsrelatedtoSMS,contacts,location,camera,andmicrophoneaccess,whichmayindicateapotentialprivacybreach. 2.DynamicAnalysis:Unlikestaticanalysis,thismethodinvolvesexecutingtheappinacontrolledenvironment,eitheronanemulatororanactualdevice,andmonitoringitsbehavioratruntime.TheAPIcallsmadeduringexecutionareloggedandanalyzedtodetectanysuspiciousormaliciousbehavior,suchasexcessivenetworkcommunication,unauthorizedfileaccess,ortheuseofobfuscatedtechniques. 3.MachineLearningApproach:MachinelearningalgorithmscanbetrainedonadatasetcontainingbothmaliciousandbenignappstoclassifyappsbasedontheirAPIcallpatterns.Byanalyzingthefrequency,sequence,andcontextofAPIcalls,thesealgorithmscanidentifyandclassifyappsexhibitingmaliciousbehavior. 4.Behavior-basedAnalysis:Thisapproachinvolvesmodelingtheexpectedbehaviorofanappandcompari