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基于深度学习的Windows系统恶意软件检测研究.docx

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基于深度学习的Windows系统恶意软件检测研究 Title:ResearchonWindowsMalwareDetectionbasedonDeepLearning Abstract: Therapidgrowthofcyberthreatshasnecessitatedthedevelopmentofeffectivemalwaredetectiontechniques.Traditionalapproachesoftenstruggletokeeppacewiththeevolvingsophisticationofmalware.ThispaperpresentsaresearchstudyonusingdeeplearningalgorithmsforWindowssystemmalwaredetection.Byleveragingthepowerofdeepneuralnetworks,thisresearchaimstoenhancetheaccuracyandefficiencyofdetectingmalicioussoftware. Introduction: TheprevalenceofmalwarehasposedsignificantchallengestothesecurityofWindowsoperatingsystems.Traditionalsignature-basedmalwaredetectiontechniquesrelyonknownpatternsandsignatures,whichareincapableofdetectingpreviouslyunknownorzero-daymalware.Asmalwaretechniquesconstantlyevolve,moreadvancedapproachesarerequiredtodetectandmitigatethesethreats.Deeplearning-basedapproacheshaveshownpromisingpotentialinimprovingmalwaredetectionaccuracy.ThisresearchfocusesoninvestigatingtheperformanceofdeeplearningalgorithmsforWindowssystemmalwaredetection. Background: 1.WindowsMalware:ThissectionexploresthedifferenttypesandcharacteristicsofmalwarethatcommonlytargetWindowssystems.Itdiscussesthechallengesposedbythesemalware,suchasstealthybehavior,polymorphism,andobfuscationtechniques. 2.TraditionalMalwareDetectionTechniques:Thissectionprovidesanoverviewofconventionaltechniquesutilizedformalwaredetection,includingsignature-baseddetection,heuristicanalysis,andbehavior-baseddetection.Thelimitationsofthesetechniquesandtheirvulnerabilitytosophisticatedmalwarearediscussed. 3.DeepLearning:Thissectionintroducestheconceptofdeeplearninganditssignificanceinvariousdomains.Thefundamentalsofdeepneuralnetworks,includingconvolutionalneuralnetworks(CNNs),recurrentneuralnetworks(RNNs),andlongshort-termmemory(LSTM),areexplained.Theadvantagesofdeeplearningformalwaredetection,suchastheabilitytolearncomplexpatternsandfeatureextraction,arediscussed. Methodology: Thissectionoutlinestheresearchmethodologyadoptedtoachievetheobject