利用生成性人工智能進行記憶分析

LeveragingGenerativeArtificialIntelligenceforMemoryAnalysis

Author:WilliamCopeland,wcopeland1981@Advisor:TimProffitt

Accepted:May28,2024

Abstract

Theincreasingsophisticationofmalwareposessignificantchallengesfortraditional

memoryanalysistechniquesindigitalforensics.Thisresearchexploresthepotentialof

leveragingGenerativeArtificialIntelligence(AI)models,specificallyOpenAI’sGPT-4

TurboandAnthropic’sClaude3Opus,toenhancemalwaredetectioninmemory.By

combiningthedataextractioncapabilitiesoftheVolatilityFrameworkwiththepredictivepowerofGenerativeAI,thisstudyaimstodevelopaninnovativeapproachforaccuratelyidentifyingandclassifyingmaliciousactivitiesinmemorydumps.Theresearch

methodologyinvolvescollectingadiversesetofmemorydumpsamples,preprocessingthedatausingVolatilityplugins,andevaluatingtheperformanceoftheAImodelsusingquantitativemetrics.

ThefindingshighlightthepotentialofGenerativeAImodelsineffectivelyidentifying

malware,whilealsorevealinglimitationsandareasforimprovement.Theimplications

suggestthatGenerativeAImodelscanserveasvaluablecomplementarytoolsalongsidetraditionalmalwaredetectionmethods,andfutureresearchrecommendationsinclude

expandingdatasets,developingdomain-specificmodels,andintegratingGenerativeAI

capabilitiesintoexistingmemoryforensicsworkflows.ThisstudylaysthefoundationforfurtherexplorationandadvancementofGenerativeAImodelsin-memoryanalysisand

malwaredetection.

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1.Introduction

Indigitalforensics,memoryanalysisiscrucialinuncoveringvaluableevidence

andinsightsfromcomputersystems.Memorydumps,snapshotsofacomputer’svolatilememoryataspecifictime,containawealthofinformationaboutrunningprocesses,

networkconnections,andsystemactivities.However,malware’sincreasing

sophisticationandcomplexityposesignificantchallengesfortraditionalmemoryanalysistechniques.Researchhasshownthatmalwarehasreachedalevelofsophistication,

makingdetectionandanalysisextremelydifficultforforensicinvestigatorsandincidentrespondersduetomalwareauthorsemployingvariousobfuscationandevasion

techniques(Kolbitsch,etal.,2009).

Researchersandpractitionershaveturnedtoadvancedtoolsandframeworksto

addressthesechallenges,suchastheVolatilityFramework.TheVolatilityFrameworkisawidelyusedopen-sourcememoryforensicstoolthatprovidesacollectionofpluginsforextractingandanalyzingdatafrommemorysamplesfromdifferentoperationsystems

(TheVolatilityFramework,2024).However,theeffectivenessofmemoryanalysisheavilyreliesontheabilitytoaccuratelyidentifyandclassifymaliciouspatternsandbehaviorsintheextracteddata.

TheGenerativeArtificialIntelligence(AI)fieldhaswitnessedsignificant

advancementsinrecentyears,openingnewdataanalysisandpatternrecognition

possibilities.ModernGenerativeAImodelsincludeOpenAI’sGPT-4Turboand

Anthropic’sClaude3Opus.GPT-4Turboisalargemultimodalmodelwithalarger

contextwindowof128,000tokens,whichacceptstextorimagesasinputstoproducea

textoutput.Withbroadergeneralknowledgeandadvancedreasoningcapabilities,the

modelcanmoreaccuratelysolvecomplexproblems(Models,2024).Claude3Opusis

Anthropic’smostpowerfulmultimodalmodel,whichdeliversadvancedperformanceanddemonstratesahuman-likeunderstandingoftext(ModelsOverview,2024).Withan

extendedcontextwindowof200,000tokens,themodelcanprocesslargeamountsofdatatocompletehighlycomplextasks(LongContextWindowTips,2024).Toutilizethese

GenerativeAIcapabilities,apaidsubscriptionisrequiredtointeractwiththemodelviawebinterfaceoraccesstothemodelviaApplicationProgrammingInterface(API).

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Overall,bothmodelsarepromisingcandidatesforapplicationinvariousdomains,includingcybersecurityanddigitalforensics.

ThisresearchexploresthepotentialofleveragingGenerativeAImodelsfor

enhancedmalwaredetectioninmemory.BycombiningthedataextractioncapabilitiesoftheVolatilityFrameworkwiththepredictivepowerofGenerativeAI,aninnovative

approachwillbedevelopedthatcanaccuratelyidentifyandclassifymaliciousactivitiesinmemorydumps,ultimatelystrengtheningthecapabilitiesofforensicinvestigatorsincombatingsophisticatedcyberthreats.

2.ResearchMethod

2.1.QuantitativeAnalysisMethod

Thisresearchwillutilizetheaccuracyanderrorratesmethodtoevaluatethe

performanceoftheGenerativeAImodels,specificallyOpenAI’sGPT-4Turboand

Anthropic’sClaude3Opus,inidentifyingmalwarewithmemorydumps.This

quantitativeanalysismethodprovidesastraightforwardapproachtomeasuringthe

overallproportionofsamplescorrectlyclassifiedbythemodel,whichaidsinidentifyingareasofimprovementforthemodel.Keymetricsarecalculatedbycomparingthe

models’predictionswiththegroundtruthlabels,suchasaccuracyanderrorrates,whichconsistoftheFalsePositiveRate(FPR)andFalseNegativeRate(FNR),toassessthe

effectivenessofmalwaredetection.Accuracy,asseeninFigure1,isthemeasurementoftheoverallcorrectnessofthemodelsinidentifyingmaliciousandbenignmemorydumps.

Accuracy=(TruePositive(TP)+TrueNegative(TN))/(TotalInstances)

Figure1:AccuracyCalculation

FPR,asseeninFigure2,calculatestheproportionofbenignsamplesincorrectlyclassifiedasmalicious.

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FPR=FalsePositive(FP)/(FalsePositive(FP)+TrueNegative(TN))

Figure2:FalsePositiveRateCalculation

FNR,asseeninFigure3,calculatestheproportionofmalicioussamplesincorrectlyclassifiedasbenign.

FNR=FalseNegative(FN)/(FalseNegative(FN)+TruePositive(TP))

Figure3:FalseNegativeRateCalculation

Thesemetricsprovideameanstoevaluatethemodels’performanceandhelpidentifytheirstrengthsandweaknessesinthecontextofmemoryforensics.

2.2.DataCollection

Thedatacollectionphaseinvolvedacquiringdiversememorydumpsamplesandcategorizingthemasmalicious,benign,orunknown.Toensurethereliabilityand

representativenessofthedataset,memorydumpsfromvarioussources,suchaspubliclyavailabledatasets,malwarerepositories,andreal-worldincidents,willbecollected.

TheVolatilityFrameworkwillextractvaluableinformationfromasystemduringmemoryacquisition,suchasprocesses,loadmodules,handles,andnetworkconnectionsfromeachmemorydumpsample(TheVolatilityFramework,2024).TheVolatility

plugin’soutputcreatesarichsetofartifactsanddatapointstoserveasinputforthe

GenerativeAImodels.ThreeartifactcategoriesofVolatilitypluginswereutilizedduringthisresearch:processes,networkconnections,andsuspiciousactivity.Figure4outlinesthepluginsforeachcategory.

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VolatilityFrameworkPlugins

Processes

NetworkConnections

SuspiciousActivity

pslistpsscan

netscan

malfindapihooks

cmdline

timeliner

dlllist

ldrmodules

handles

Figure4:VolatilityPlugins

Thesepluginswerechosenbasedontheirrelevancetomemoryanalysisandtheirabilitytoprovidevaluableinsightsintopotentialmaliciousactivities.

2.3.DataProcessing

Severalpreprocessingstepsmustbeperformedtopreparethecollecteddatafromthememorydump.First,theoutputfromtheVolatilitypluginsmustbecleaned,

aggregated,andconvertedintoastructuredformat,specificallyJavaScriptObjectNotation(JSON).Thisstandardizedformatwillfacilitatethedataingestionintothemodelsandensureconsistencyacrossdifferentmemorydumpsamples.

Next,alabelisappendedtothepreprocesseddatawiththesamplename,

operatingsystem,andthegroundtruthinformationforeachmemorydump.Thegroundtruthlabelswillclassifyeachsampleasmaliciousorbenign,providingareliable

referenceforevaluatingtheperformanceofthemodels.Figure5isthegroundtruthtableutilizedforthesamples.

Malicious

Benign

Sample-1

True

False

Sample-2

True

False

Sample-3

False

True

Sample-4

True

False

Sample-5

True

False

Sample-6

False

True

Figure5:GroundTruthTable

Furthermore,developinganappropriatesystempromptiscrucialforeffectivelyutilizingthemodels.Thepromptisdesignedtoelicitrelevantinformationfromthe

modelsbasedonthepreprocesseddata,enablingthemodeltomakeapredictionand

providemeaningfulinsights.Figure6isthesystempromptutilizedduringtheresearch.

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Youareanincidentresponderanalyzingamemorydumpfromapotentially

compromisedWindowssystemusingtheVolatilityframework.YouwillbeprovidedwiththeconsolidatedoutputoftheVolatilitypluginsrunagainstthememorydump.

YourtaskistocarefullyanalyzetheprovidedVolatilitypluginoutputtolookforevidencethatisofmalwareonthesystem.

Usethestep-by-stepinstructionsbelowtobuildaresponsetotheuser’sinput.

Step1-Theuserwillprovideyouwithdata.Providethefollowinginformation:<name>Providethesamplefilename.

<operatingsystem>Providetheoperatingsystemidentifiedinthefile.<key>Providethekeytothelabelkeypairinthedata.

<answer>InformtheuserifthedumpisMalicious,Benign,orUnknown.Onlyprovideoneanswer.

SampleName:<name>

OperatingSystem:<operatingsystem>

TruthLabel:<key>Prediction:<answer>

Step2-WriteoutyouranalysisandreasoningbasedontheVolatilityoutput.CitespecificlinesfromtheVolatilityoutputtojustifyyourreasoningwhereapplicable.

Figure6:SystemPrompt

2.4.ModelEvaluation

Evaluatingthemodelsrequiresasystempromptandingestingthepreprocesseddataintothemodelstoassesstheirperformanceusingquantitativemetrics.The

OpenAI’sGPT-4TurboandAnthropic’sClaude3Opusmodelswereemployedforthispurpose,leveragingtheiradvanceddataanalysiscapabilitiestoanalyzethememory

dumpdata.

ThepreprocesseddatawasingestedintoOpenAI’sGPT-4TurboandAnthropic’sClaude3Opusmodelsusingtheirrespectivedeveloperplatforms,OpenAI’sPlayground(Playground,2024)andAnthropic’sWorkbench(Workbench,2024).Theseweb-basedinterfacesprovideauser-friendlyenvironmentfordeveloperstosubmitdataandinteractwiththemodelsthroughtheirAPIs.Theplatformsallowforconfiguringmodel

parameters,suchastemperatureandtokenlimit,andprovideameanstoinputthesystempromptanddata.Uponsubmission,themodelsprocesstheinputandgeneratearesponsecontainingtheiranalysisandclassificationofthememorydumpsamples.Themodel’s

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classificationsarethencomparedagainstthegroundtruthlabelsassociatedwitheachmemorydumpsampletoevaluatethemodels’performanceandcalculatetheaccuracy,FPR,andFNRmetrics.Bycomparingtheevaluationmetricsbetweenthemodels,the

researchwillshowinsightsthatcanbegleanedintotherelativestrengthsandweaknesses.

Insummary,theresearchmethodoutlinedinthissectionprovidesanapproachtoevaluatingtheeffectivenessofeachmodel.ItaimstocontributetotheadvancementofintegratingGenerativeAIintoforensicstechniquestocombatevolvingmalwarethreats.

3.FindingsandDiscussion

3.1.AnalysisofQuantitativeResults

Beforedivingintothequantitativeanalysisofthemodel’sperformance,itis

necessarytounderstandthenatureofthedatasetfedintothemodels.Theinputdatasetconsistedofextractedartifacts(runningprocesses,networkconnections,andsuspiciousmemoryregionswereextracted)frommemorysamples.Thesesamplesencompassedavarietyofmalwarefamilies,suchasransomware,trojans,androotkits,aswellasbenignmemorydumpsfromcleansystems.Includingmaliciousandbenignsampleswas

essentialtoaccuratelyassessthemodels’abilitytodistinguishbetweenthetwoclasses.

Itisimportanttonotethatthedatasetusedinthisanalysiswaslimitedtosix

samplesbutcontainedverycomplexdata.Thedatasetrepresentedvariousscenarios,

encompassingoperatingsystems,systemconfigurations,andmalwarebehaviors.The

complexityofthedatasetposedasignificantchallengeforthemodels,astheyneededtoidentifysubtlepatternsandindicatorsofcompromiseamidstavastamountofmemory

data.Byfeedingthemodelswithsuchacomprehensiveandchallengingdataset,theaimwastoassesstheirabilitytogeneralizeandaccuratelydetectmalwareinmemorydumps.

Thequantitativeanalysisofthemodels’performanceyieldedmeaningfulinsights.Thetruthtableandmodelpredictionsforeachsamplewereusedtocalculatethe

accuracy,FPR,andFNRforboththeGPT-4TurboandClaude3Opusmodels,refertoAppendixA–F.Figure7,AccuracyandErrorRates,consolidatesandprovidesthe

metricsforbothmodels.

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Accuracy

1

FalsePositiveRate

0

FalseNegativeRate

0

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OpenAIGPT-4Turbo

AnthropicClaud3Opus

TruePositives(TP)

4

TruePositives(TP)

6

TrueNegatives(TN)

0

TrueNegatives(TN)

0

FalsePositives(FP)

2

FalsePositives(FP)

0

FalseNegatives(FN)

0

FalseNegatives(FN)

0

TotalSamples

6

TotalSamples

6

Accuracy

0.6666667

FalsePositiveRate

1

FalseNegativeRate

0

Figure7:AccuracyandErrorRates

Theaccuracy,FPR,andFNRvaluesrangefrom0to1,providingastandardizedmetricforevaluatingthemodels’performance.Accuracyrepresentstheoverall

correctnessofthemodelsinclassifyingmemorydumpsasmaliciousorbenign.Itis

calculatedbydividingthesumoftruepositives(correctlyidentifiedmalicioussamples)andtruenegatives(correctlyidentifiedbenignsamples)bythetotalnumberofsamples.Anaccuracycloserto1isanindicationofbetteroverallperformance.

Inthisanalysis,theGPT-4Turbomodelachievedanaccuracyof0.6666667,

whichmeansitcorrectlyclassifiedapproximately66.67%ofthesamples.Ontheotherhand,theClaude3Opusmodelachievedanaccuracyof1,indicatingthatitcorrectlyclassifiedallthesamplesinthedataset.

TheFalsePositiveRate(FPR)representstheproportionofbenignsamples

incorrectlyclassifiedasmalicious.Itiscalculatedbydividingthenumberoffalse

positives(benignsamplesincorrectlyidentifiedasmalicious)bythesumoffalse

positivesandtruenegatives(correctlyidentifiedbenignsamples).AnFPRcloserto0isdesirable,asitindicatesalowerrateoffalsealarms.TheGPT-4Turbomodel

demonstratedanFPRof1,incorrectlyclassifyingallbenignsamplesasmalicious.ThishighFPRsuggeststhattheGPT-4Turbomodeltendstogeneratefalsepositives,

potentiallyleadingtounnecessaryinvestigationeffortsandresourceallocation.In

contrast,theClaude3OpusmodelachievedanFPRof0,indicatingthatitdidnot

misclassifyanybenignsamplesasmalicious,whichisidealforreducingfalsealarms.

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TheFalseNegativeRate(FNR)representstheproportionofmalicioussamples

incorrectlyclassifiedasbenign.Itiscalculatedbydividingthenumberoffalsenegatives(malicioussamplesincorrectlyidentifiedasbenign)bythesumoffalsenegativesand

truepositives(correctlyidentifiedmalicioussamples).AnFNRcloserto0ispreferred,asitindicatesalowerrateofmissedmalware.TheGPT-4TurboandClaude3Opus

modelsdemonstratedanFNRof0,indicatingthattheycorrectlyidentifiedallthe

malicioussamplesinthedataset.ThisperfectFNRsuggeststhatbothmodelscandetectmalwarewithoutmissinganymaliciousinstances.

However,itiscrucialtoconsiderthelimitationsofthedatasetusedinthis

analysis.Thedatasetconsistsofalimitednumberofsamples,anditisvitaltoevaluatethemodels’performanceonamoreextensiveanddiversedatasettoassesstheir

generalizationcapability.Additionally,themodel’sabilitytodetectunseenmalwaresamplesinreal-worldscenariosshouldbevalidatedtoensuretheireffectivenessincategorizingsophisticatedandevolvingmalware.

RefertoAppendixA-FtoreviewthecompleteresponsefromGPT-4TurboandClaude3Opusmodelsforthesampledatasubmitted.

3.2.Limitations

Despitethepromisingresults,itisvitaltoacknowledgethelimitationsofthe

currentresearch.Onelimitationisthesizeanddiversityofthedatasetusedfor

evaluation.Whileeffortsweremadetocollectarepresentativesampleofmemorydumps,thedatasetdoesnotencompassallpossiblevariationsandemergingmalwaretechniques.Futureresearchshouldexpandthedatasetandincludeamorecomprehensiverangeof

malwarefamiliesandsystemconfigurations.

Anotherlimitationwastheconstraintposedbythecontextwindowsizeofthe

GPT-4TurboandClaude3Opusmodels.TheClaude3Opusmodelhasasignificantly

largercontextwindow,capableofprocessingupto72,000moretokensthantheGPT-4Turbomodel.ThisdifferenceintokencapacitypresentedchallengeswhensubmittingthedatageneratedbytheVolatilityplugins.Duetotheextensiveamountofdataproduced,

thetokenlimitofthemodelwasexceeded.Aniterativeprocesswasemployedto

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reconfigurethepluginselectiontoensurethatthedatageneratedbytheVolatilitypluginsdidnotexceedthetokenlimitofthemodels.Theobjectivewastofindanoptimal

balancebetweencollectingacomprehensivesetofartifactsandreducingtheoveralldatasize.Theprocessinvolvedexchangingpluginsmultipletimesandevaluatingtheimpactonthetokencount.Aftereachreconfiguration,theresultingdatawassubmittedto

OpenAI’sTokenizer(Tokenizer,2024)andHuggingFacesTokenizertool(TokenizerArena,2024),whichcalculatedthetokensizeofthedata.Thisiterativeprocesswas

repeateduntilthedataforeachmemorydumpsamplewaswithinthe128,000tokens,

ensuringthedatadidnotexceedthecontextwindowforbothmodels.Asaresultofthisoptimization,theinitialsetoftenpluginshadtobenarroweddowntoasubsetofsix

plugins.Figure8presentstherefinedlistofpluginsusedinthefinalanalysis.

VolatilityFrameworkPlugins

Processes

NetworkConnections

SuspiciousActivity

pslistpsscan

netscan

malfindapihooks

cmdline

Figure8:VolatilityPlugins

Thisreductionallowedforthesuccessfulsubmissionofthedatatobothmodelswithoutexceedingtheirtokencapacities.However,italsomeantthatsomepotentiallyvaluableinformationfromtheexcludedpluginscouldnotbeincorporatedintotheanalysis.Thislimitationhighlightsthetrade-offswhenworkingwithAImodelswithdifferentcontextwindowsizes.

Moreover,themodelsusedinthisresearch,GPT-4TurboandClaude3Opus,haveinherentlimitations.Thesemodelsarebasedonnaturallanguageprocessingandmaynotbedesignedexplicitlyformalwaredetectioninmemorydumps.Further,fine-tuningandadaptingthemodelstothespecificdomainofmemoryforensicscould

enhancetheirperformance.

3.3.AreasofImprovement

Severalareasforimprovementcanbeidentifiedbasedonthefindingsand

limitationsdiscussed.First,thepreprocessingtechniquesappliedtothememorydump

datacouldbefurtherrefined.Exploringtechniquestoefficientlycompressorsummarize

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thedatageneratedbytheVolatilitypluginswouldenabletheinclusionofmorecomprehensiveinformationwithinthetokenlimitsofthemodels.

Secondly,thepromptsusedtointeractwiththemodelscanbeoptimized.

Developingmoretargetedandcontext-specificpromptscouldimprovethemodels’

abilitytoidentifymaliciouspatternsaccurately.PromptdevelopmentiscrucialingettingthemostoutofGenerativeAImodels.Researcherscanguidethemodelsbycarefully

designingpromptstoproducemoreaccurateandrelevantresponsesformalwaredetectionandothertasks.

Whenoptimizingprompts,severalkeyfactorsshouldbeconsidered.Thepromptshouldhaveaclearobjective,explicitlystatingwhatthemodelshouldachieve(PromptEngineering,2024).Providingspecificdetailsorparametersrelevanttothetaskcan

furtherguidethemodel’sresponse.Usingconciseandunambiguouslanguagereduces

confusionandimprovesthemodel’sunderstandingofthetask.Includingrelevant

context,framingthepromptasaquestionordirective,andspecifyingtheroleorpersonathemodelshouldadoptcanalsohelprefinetheresponsestyleanddepthaccordingtotheassumedexpertise(PromptEngineering,2024).Breakingcomplextasksintoclear,

manageablestepsandprovidingexamplescanleadtomorestructuredandcoherentresponses(PromptEngineering,2024).

Promptdevelopmentisaniterativeprocessthatrequiresexperimentationand

refinement.Researchersshouldcontinuouslyevaluatethemodel’sresponsesandadjustthepromptsaccordingly.Well-craftedpromptscansignificantlyenhancethemodels’

abilitytoidentifymaliciouspatternsaccuratelyinthecontextofmalwaredetection.Bycarefullydesigningpromptsthatalignwitheachtask’sspecificobjectivesand

requirements,researchersandpractitionerscanharnessthepowerofthesemodelstosolvecomplexproblemsandgeneratevaluableinsights.

Additionally,incorporatingdomain-specificknowledgeandrulesintothemodelscouldenhancetheirperformance.Byintegratingexpertknowledgeandheuristicsfrom

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memoryforensics,themodelscanbeguidedtowardamoreaccurateandcontext-awareprediction.

Furthermore,exploringensembletechniquesthatcombineGenerativeAImodelsorintegratingthemwithtraditionalmalwaredetectionmethodscouldprovideamore

robustapproachtoidentifyingmalwareinmemorydumps.

3.4.ReflectionontheResearchObjective

Theresearchobjectivessetoutatthebeginninghavebeensuccessfullyaddressed.Thequantitativeanalysismethod,utilizingaccuracyanderrorrates,providedameanstoevaluateGenerativeAImodels’performanceindetectingmalwareinmemorydumps.

Thedatacollectionandpreprocessingsteps,leveragingtheVolatilityFrameworkandselectedplugins,enabledtheextractionofartifactsfrommemorydumps.The

preprocesseddatawasinputfortheGenerativeAImodels,facilitatingtheirevaluationandanalysis.

Thefindingsanddiscussionsectionprovidedin-depthinsightsintothemodels’

performance,limitations,andareasforimprovement.Theimplicationsofthefindingsforenhancingmalwaredetectioninmemorywereexplored,highlightingthepotential

benefitsandchallengesassociatedwithintegratingGenerativeAImodelsinmemoryforensics.

4.ImplicationsandRecommendations

4.1.ImplicationsoftheFindings

Thefindingsofthisresearchhavesignificantimplicationsforimprovingmalwaredetectioninmemory.ThegenerativeAImodels’highaccuracyandrelativelylowerrorratesdemonstratetheirpotentialasavaluabletoolformemoryforensicspractitioners.

ByleveragingthepowerofGenerativeAI,investigatorscanautomateand

acceleratetheprocessofanalyzingmemorydumps,enablingthemtoidentifypotential

malwareinfectionsquickly.TheexperimentalresultsinthisstudyshowthattheClaude3Opusmodelachievedaperfectaccuracyscoreof1andanFNRof0,indicatingitsabilitytocorrectlyidentifyallmalicioussampleswithoutmissingany(Figure7).Thislevelof

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accuracycanconsiderablyreducethetimeandenergyrequiredformanualanalysis,

allowingformoreefficientandeffectiveincidentresponse.ResearchhasshownthatAI-basedtechnologiesleveragedindigitalforensicinvestigationscandrasticallysavethe

timeneededtoevaluateanduncoverpotentialsecuritybreaches(Fakiha,2023).

Moreover,theabilityofgenerativeAImodelstolearnandadapttonewmalwarepatternsandtechniquesopenspossibilitiesforproactivethreatdetection.Asnewmalware

variantsemerge,thesemodelscanbecontinuouslytrainedandupdatedtodetectevolving

threats,enhancingorganizations’overallsecurityposture.Researchsuggeststhat

organizationsmustemploycontinuouslearningandadaptivemodelstokeeppacewith

theever-evolvinglandscapeofmalwarethreats(Sindiramutty,2023).However,itis

crucialtoconsiderthelimitationsandpotentialrisksassociatedwithrelyingsolelyon

GenerativeAIformalwaredetection.Falsepositivesandnegativescanhavesignificanteffects,suchaswastedresourcesonbenignsamplesoroverlookingthreats.Inthisstudy,theGPT-4Turbomodel’shighFPRof1.0(Figure7)highlightstheneedforcautionandfurtherrefinement.Therefore,itisrecommendedthatGenerativeAImodelsbeusedasacomplementarytooltoworkalongsidetraditionalmalwaredetectionmethodsandhumananalysis.

4.2.RecommendationsForFutureResearch

Basedontheoutcomesofthisstudy,severalrecommendationsforfutureresearchcanbemadetoenhancetheeffectivenessandapplicabilityofGenerativeAImodelsin

memoryforensics.TheserecommendationsfocusonaddressingthelimitationsidentifiedinthecurrentstudyandexploringnewavenuestofurtheradvancethefieldofAI-assistedmemoryforensics.

4.2.1.ExpandedDataset

Oneofthecriticallimitationsofthecurrentstudyisthelimitedsizeofthedataset,whichconsistedofonlysixmemorydumpsamples.Whilethisdatasetallowedforan

initialevaluationoftheGenerativeAImodels’performance,futureresearchshouldaimtocollectamoreextensiveanddiversedatasettoenhancethemodel’sgeneralization

abilityandrobustness.Expandingthedatasettoincludehundredsorthousandsofmemorydumpsamplesfromvarioussourceswouldprovideamorecomprehensive

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representationofreal-worldscenar