2024利用生成式AI增強自主系統(tǒng)的安全性和韌性白皮書（英文版）-42正式版

EnhancingSecurity,Resilience,and

SafetyofSystemswith

GenerativeAITableofSidebar:GenAImodels4GenAIUseCases9IndividualApplications9Self-Awareness10Anomalydetection11Autonomy12Adaptability12Predictivemaintenance13Faultmanagement14Fleet16Swarmintelligence17Swarmcoordination19Communicationresilience21Consensus22Human23Missionplanningandexecution24Human-machineteaming25Meta-learning26Datalabelingsynthesis27Cybersecurity28Malware29Intrusiondetection:31Integratingthreatintelligence33Policymanagement33ThreatSimulation:34Softwaresupplychainvisibility34Challenges34Cost35Compute36Adaptation36Ethical/Regulatory36Alignment36Privacy36Accuracy3737securityissues3838Glossary41Rapidadvancesautonomoussystemsedgeroboticsunlockedunprecedented

opportunitiesinindustriesfrommanufacturingtransportationhealthcareandexploration.

Increasingcomplexityandconnectivityhaveintroducednewchallengesinensuringsecurity,

resilience,andsafety.Asedgerobotsintegrateourdailylivesandcriticalinfrastructures,itis

imperativethatwedevelopinnovativethatcanthesetrustworthiness

andreliabilitynewlevels.ThiswhitepaperexploresthetransformativepotentialofgenerativeAI(GenAI)enhancethe

security,resilience,andsafetyofautonomoussystemsandedgerobots.canusethese

cutting-edgetechnologiesmeettheuniquedistributeddynamicchallengesofedge

robotics,unlocklevelsofintelligence,adaptability,androbustness.GenAImodelsproducenewcontentbyanalyzingpatternsinadataset.Theyderivecharacteristic

probabilitydistributionsandapplythesecreatenewdatapatternsthatareconsistentwiththe

original“real”dataset.EarliergenerationsofdiscriminativeAImodelsappliedconditionalprobabilitiesoutcomesforpreviouslyunseendata.Theapproachisversatileandwell-suitedawiderange

ofproblems,includingclassificationsandregressions.Theyexcelatdelineatingthedecision

boundariesthatdifferentiatebetweenvariousclassesorcategorieswithinthedataset.Thegrowingranksofgenerativetechniquesincludethosebasedontransformersandthe

resultinglargelanguagemodels(LLMs),GenerativeAdversarialNetworks(GANs),Autoencoders(VAEs),GenerativeFlowModelsandGenerativeDiffusionModels(GDM).

ThesehaveallexcitingavenuesofAIresearch—withapplicationsdroneswarms

andintrusiondetection,physicalsecurity,semanticcommunication,andnetwo1TheTechnologyInnovationInstitute’sSecureResearchCenter(TII-SSRC,AbuUAE,https://www.tii.ae/secure-systems)isapplyGenAIitsworkextendingZero

Trustarchitectures(developedforinformationsecurity)everyaspectofinformationsecurityin

cyber-physicalsystems.Thus,SSRCconsidershowGenAIcanhelpguaranteesecurity,resilience,andforswarms,swarmsofswarms,autonomousterrestrialmarinevehicles,commandsystems,human/droneinteractionsandparticularlyinareaswhereGenAIoutperformstraditionalapproaches.Examples?Individualapplications:healthmonitoring,stateestimation,predictivemaintenance,

anomalydetection,self-healing,navigation,andemergencylandings.

?Fleetapplications:Swarmcoordination,intelligence,collectivedecision-making.

?Human/Droneinteractions:communicationresilience,missionplanning,human-

computerinteraction.?Cybersecurityandresilience:Intrusiondetection,malwareclassification,threatsimulation.ThispaperwillfocusondronesSSRCisdoingsomuchworkinthisarea.Whatwe

learnfromdronescanbeappliedmoreautonomousandcyber-physicalsystemsin

general,includingcars,robots,embeddedandsmartcities.Bythesametoken,

lessonslearnedintheseareascanbefoldedSSRC’sresearch.approachallowsorganizationsmoveawayfromphysicaldevicemanagementapproaches

thatrequireemployeescarrymultiplephones.1M.“UnleashingthePowerEdge-CloudGenerativeinMobileNetworks:AAIGCServices.”arXiv,2023.doi:10.48550/arXiv.2303.16129Sidebar:GenAImodelsAsurveyofspecificgenerativeAImodelingtechniques,withtheirstrengthslimitationsas

applieddronesafety,security,andresilience.PopularexcitementovergenerativeAImodelsbeendrivenbyhighlypublicizedlikeChatGPT,whichcreateauthoritative-soundingresponsestextpromptsusingspecially

trainedLLMs.LargeLanguageareAIsystemstrainedonvasttextdatasets.TheyuseDeepLearning

techniques,particularlyastructureknownasaTransformer,“understand”andhuman-liketextbasedonthepatternslearned.LLMsanalyzetherelationshipscontextsinthedata.Theyuseavarietyoftechniquesbuildsimplifiedrepresentations

ofthedata,allowingthemmakeassociationsandcorrelationsbetweenoriginalelements.Thesemodelsletthemcomposeresponsesthatcanmimichumanwritingstylescoverdiversetopics.VisuallikeDALL-EandStableDiffusion,compileimagesfromtext

andimageprompts.LLMsarewidelyused,widelyuseful,increatingcontent,code,translations,summaries,

syntheticdata,structureunstructuredfromtexts,documents,images,andpromptdata.Transformermodelsandtheservicesbuiltonthem—likeOpenAI’sGoogle’sGemini,

andAnthropic’sClaude—attractedwidespreadattention,thankstheirimpressiveability

createarticulate-seemingresponseshumanprompts.These,andotherdomain-specific

LLMsandSmallLanguageModels(SMLs),showpromiseforsupportinganalysis,research,

anddevelopmentimprovedronesafety,securityandresilience.ParallelingtheseveryvisibleAIdevelopments,however,hasbeenalmostadecadeofprogress

onnewclassesofGenerativeAImodelscouldautomateandrepresentation-building.

Whilegenerativeapplicationsattractthemostattentions,thesenewmodelsaredriving

advancesinanalyzingdataandinteractingwiththeworldus.OthergenerativeAI

models—GenerativeAdversarialNetworks,VariationalAutoencoders,GenerativeDiffusion

Models,NormalizingFlowModels—thoughrelativelyunknown,makesubstantial

contributionsdronesecurity,safety,andresilience.TransformerModels:Introducedin2017translatebetweenandtexts,

TransformerModelsatcapturinglong-rangedependenciescorrelationswithin

unstructureddata2Transformersleverageanovel“attentionmechanism”learnthe

connectionsbetweenwordshelpcreateembeddingsautomatically.Priortechniquesrequired

translatingrawtextintoavectorrepresentationusingaseparatemodel.Transformerscanbuild

complexrepresentationsandlearnintricateconnectionsthroughlayeredarchitecture,

manner.,allowingresearchersprocesslargebodiesofunlabeledtextdeveloplarge

languagemodelsbillionsofparameters.Subsequentinnovationssupporteddocument

summarization,composingquestion/answerassociationsacrosslargedatasets,code

generation,in-depthanalysis,intrusiondetection,malwaredetection,translatingcontrol

systeminstructionsacrossroboticarms.Thekeyadvantageisdistillingcontextfrom

complexdatasets.Challengesincludehallucination,longertrainingtime,slowerinference-

building,heaviercomputationrequirements,andlargermodelcomparedother

techniques.2Vaswani“AttentionAllNeed.”arXiv,Aug.2023.doi:10.48550/arXiv.1706.03762.GenerativeAdversarialNetworks(GANs):Theseweredevelopedin2014createrealistic

syntheticnumbers,faces,andanimalimage3GANspittwonetworksagainstother:

oneisrewardedforgeneratingrealisticcontent,andthesecondisrewardedfordetecting

fakecontent.thiscompetition,thegeneratorimprovesitscreateoutputscanfoolthediscriminator.GANsarewidelyusedincontentgeneration.Sincethefirstversions

weredesignedworkwithimages,researchersarenowfindingcreativewaystranslatesuchascodeornetworklogs,intoimagessuitableforprocessing.GANsaregoodfor

realisticsyntheticdatasetsthatcanbeusedimproveautonomoussystemsandcybersecurity

algorithms.They,too,however,sufferfromfailureslikecollapseorcatastrophic3J.Goodfellow“GenerativeAdversarialNetworks.”arXiv,Jun.2014.doi:10.48550/arXiv.1406.2661.VariationalAutoencoder(VAE):VAEswereintroducedin2014improveinferencesdrawn

fromacontinuouslyvaryingdatastream.4Thetechniquehelpsfindefficientwaysrepresent

dataandbecompressdataordetectanomaliesthreats.TrainingVAEsprocess

involvesteachingasetofencodersanddecoderstranslaterawdataintoanintermediatelatent

spacewithadifferentprobabilitydistribution.canbeusedindependentlyapplicationsanomalydetection,designingencodingschemes,dataaugmentation,generation.addition,theyareoftenusedpre-structuredataforotheralgorithms,GANs,improvetheirresults.GenerativeDiffusionModel(GDF):emergedin2015improvelearning,sampling,

inferences,andevaluationsthatwereinformedbynon-equilibriumthermodynamicsmodeling5

Thetechniqueaddsnoiseasampleanimage)andautomatesthedenoising

processrevealthedata’sstructure.Slightvariationsleaddatasets.arewidelyusedinimagegenerationandcanimprovesignalclassificationvariousdroneusecases.However,thetechniquerequireshighersamplingdemands

amorecomplexarchitectureGANsandNormalizingFlow(NFMs):Thesewereintroducedbyresearchersmakecomplex

datasimplerworkwith6Thesemodelstakeeasy-to-understanddistributions,likeanormalbell

curve,andtransformthemstepbystep.Eachstepisreversible,meaningwealwaysgothestartifneeded.Thisprocess,calleda“”movesfromasimplebeginninganend4D.andM.Welling,“Auto-EncodingVariationalBayes.”arXiv,Dec.2022.10.48550/arXiv.1312.6114.5Ling,et"Diffusionmodels:Acomprehensivemethodsandapplications."ACMComputingSurveys56.4(2023):1-39./doi/10.1145/3626235

6Ivan,SimonJDPrince,MarcusA.Brubaker."Normalizingflows:introductionandreviewcurrentmethods."IEEEtransactionsonpatternanalysisand

intelligence43.11(2020):3964-3979./abstract/document/9089305/authorsresemblesthecomplicatedtargetdataset.Bydoingthis,itispossiblestudyandusethedata

moreeffectively.NFMshavebeenusedgeneratehandwrittennumbers,images,etc.Newer

usecasesincludeenhancedclassificationencodingschemes.Thetrainingprocessamodelthattransformstheprobabilitydistributionofadatasetacomplex,fully

reversibledistribution.NFMscan,however,requirehighercomputationandtrainingtimesthan

techniqueslikeGANsVAEs.ThefollowingfiguresummarizesthemainTechniquestheirapplicationsthefield

ofZeroTrustforautonomoussystems.GenAIUseTheproliferationofdrone-technologyhasbroughtchallengesspanbetweendomains

—individual,fleet,humancontrol,cybersecurity.Theirgrowthandcomplexitydemand

constantinnovationredoubletheirtrustworthinessandreliability.Thefollowingapplications—whetherderivedfromUnmannedAerialVehicle(UAV)anddrone

researchorimportedfromotherdomains—importantimplicationsforthefutureofUAVsand

otherautonomoussystems.too,thatmanyoftheseareearly-stageprojects,includedgiveaflavorwhatGenAItoolsmightaccomplishastechniquesevolve.本報告來源于三個皮匠報告站（）,由用戶Id:673421下載,文檔Id:490694,下載日期:2025-01-23GenAIshowstremendouspotentialforimprovingZeroTrustframeworksenhancesecurity,

resilience,andsafetyinindividualautonomoussystems,suchasdrones,self-drivingcars,

robots,andembeddedsystems.Usecasesunderinvestigationincludeboostingself-

awareness,anomalydetection,autonomousdriving,predictivemaintenance,faultmanagement,

self-healing,andlandingsafely.Self-AwarenessOpportunity:Efficientlytranslatenoisy,blurry,andinconsistentdataunderstandthedrone's

currentstate—e.g.,compensatingformotionblurwhiletryingdetectobstacles.

Thefoundationofdronehealthisaccuratelycapturingandofitscurrentstate—

includingtheconditionofitscurrenthardware,itsapplications,itsphysicallocation,securityposture.theworld,thiscanmessy,asvideofeedsmotionblur,datajitters,inertialguidancelosecalibration,andnoiseorgapsdegradeinternaldata.Stateestimationiscrucialautonomousnavigationanddecision-making,andrawdatastreams

mustbeaccuratelycorrelatedwithposition,velocity,andorientation.7GenerativeAIcanhelpfill

inmissingdataandfusefrommultiplesourcesimprovestateestimation.8InnovationsGenAIalgorithmslikeGANs,VAEs,andtraditionalMLalgorithmslikeLSTMfillingaps,preservingvehiclefaultdetection,predictivemaintenance,managementandsafe-landingprotocols.Forexample,innovativeapproachescanmissingandmakeiteasierfusestreamscreateamoreaccuratestate

assessment9helpcorrelatedatawithacousticanalysis,10andidentifymechanicalissue11Researcherstechniquesforgeneratingestimated-

statevariablesusingConditionalGANsforindividualdronesdroneswarms.127T.D.Barfoot,Stateestimationforrobotics.CambridgeUniversityPress,2017.8Guangyuan,NguyenVanHuynh,HongyangDinhThaiHoang,DusitNiyato,KunZhu,JiawenKang,ZehuiJamalipour,andDongInKim.“GenerativeUnmannedVehicleSwarms:Challenges,ApplicationsandOpportunities.”arXiv,February28,/10.48550/arXiv.2402.18062.

9Chai,andZ.approachstateestimationgenerativeadversarialnetwork,"inIEEEInternationalConferenceonMan(SMC),2019,2248-/document/891458510Wang,Vinogradov,“ImprovingtheperformanceconvolutionalhistoryensembleforunsupervisedearlydetectionwithacousticemissionsignalsSci.,(5)(2023),p.3136,10.3390/APP1305313611Zheng,Farahat,andC.Gupta,“GenerativeAdversarialNetworksforFailurePrediction.”arXiv,04,2019.Accessed:Mar.15,2024.[Online].Available:/abs/1910.0203412He,C.X.Tian,andW.Zeng,"AtwofoldSiamesenetworkforreal-timetracking,"inProceedingstheIEEEconferenceoncomputervisionpattern

2018,pp.4834-4843./document/8578606AnomalydetectionOpportunity:Improveanalysisofdronesensordataidentifyabnormalconditions.

Moreaccurate,multi-dimensionalsystem-staterecordscanalsohelpidentifyanomaliesrelevant

dronehealth.Forexample,VAEscanimprovefaultdetectionisolation.Theycanidentifythewarningsignsofstressinsystems,prioritizepredictiveschedules.Typically,machine-learningclassificationalgorithmstrainedonclasses

(suchastagged“faulty”faulty”).onclasses,suchasdata,isoften

scarceinpublicdatasetsandtherealworld.thesecases,GenerativeAdversarialNetworks

(GANs)canbevaluablesynthesizingtheseclasses—making“faulty”datathatlooks

conditions.addition,researchersareexploringhowcouldhelpbettercontextualizeoperatenewenvironments.13Forexample,DriveLLMcombinesLLMtraditionalautonomousnavigationalgorithmssupportbetterreasoningdecision-makingwhenrespondingedgecases.14Researchers

foundthemethodcouldimproveproactivedecision-makingunexpectedcircumstances.

Anotherapplication,TypeFly,enhancescommunicationbetweenhumansanddronesthrougha

naturallanguageinterface15.Suchlargelanguagemodelsmay,however,manifestsurfacebias,inaccuracies,and

hallucinationissuesthatrequireadditionalsafeguards.Similarly,MicrosoftResearchdiscusses

theiradvancementsinintegratingwithroboticsmakerobotcontrolintuitive

throughnaturallanguage.They'veenabledunderstandexecutetasksin

physicalenvironments,whichfacilitateseasierhuman-robotinteractionwithouttheneedfor

complexprogrammingknowledge.Theteamhasdevelopeddesignprinciplesfor

languagemodelsroboticstasks(involvingspecialpromptingstructureshigh-level

APIs),andtheyhavedemonstratedhowcanhandletasksoperatingdronesrobotarmsthroughuser-friendlycommandsfeedback.Thedevelopersemphasizethe

importanceofsafetyandsimulationtestingbeforereal-worldapplication16AdaptabilityOpportunity:Improvetranslationofautonomoussystemssoftwarerunacrossdifferenthardwaremakes,models,andconfigurations.Autonomoussystemcontrollersmustbetrainedforaspecificmodelconfiguration.Thiscan

createchallengeswhenupgradingindividualcomponentsoradoptingnewmodels.RTXisarobot

controlthattranslatecontrolpoliciesmanagedifferentroboticarmswithoutthecontrolalgorithmsforthelatesthardware.sometests,leveragingtheexperienceofcontrollers,producedcontrolpoliciestheoutperformedthebestcontrolscustom-builtforan

individuala1713Lei,surveyonlargelanguagemodelautonomousagents."FrontiersComputerScience18.6(2024):1-26./article/10.1007/s11704-024-40231-114“DriveLLM:ChartingthePathTowardFullAutonomousDrivingWithLargeLanguageModels,”IEEETransactionsIntelligentVehicles,vol.pp.1450–Jan.2024,doi:10.1109/TIV.2023.3327715.15Chen,Guojun,XiaojingYu,Zhong."TypeFly:FlyingwithLargeLanguageModel."arXivpreprintarXiv:2312.14950(2023)./pdf/2312.14950

16Vemprala,Sai,et"Chatgptforrobotics:Designandmodelabilities."arXivarXiv:2306.17582(2023)./abs/2306.17582

17X.-Collaborational.,“OpenX-Embodiment:RoboticDatasetsandRT-XarXiv,Dec.17,2023.10.48550/arXiv.2310.08864.EarlyLLMs,likeGPT3.5,weretrainedonlargebodiesoftextscrapedfromtheInternet.These

modelslackedreal-worldexperiencethatcouldreflecthowvariousconfigurationsofrobotsotherautonomoussystemsmakeandexecutedecisions.Researchintoroboticaffordances

exploreshowconstrainrobotmodelactionsthatarefeasibleandappropriateforcapabilities.18ThisprovidesaframeworkforguidingLLMdevelopmentbasedonmorecomplete

knowledgeofanoperationorprocedure.Atthesametime,thefunctiontranslateshigh-levelknowledgeintoexecutionbyaparticularrobotmodelinaspecifictargetenvironment.PredictivemaintenanceOpportunity:Predictthependingbreakdownofdronecomponentsoptimizemaintenance,repair,part-replacementschedules.Properlyandanalyzed,thedrone’ssensoroperationaldatarevealmechanicalproblemsbeforebreakdownsoccur.Predictivealgorithmsmaintenanceandrepair

crewsestablishregularschedules,prioritizemaintenance,andstayaheadofpartsinventories.

advancenoticeandplanning,evenmajorrepairsandreplacementscanbeperformedduring

routineservice.Theprobabilitiesofcostlybreakdownsand,worse,catastrophicfailuressharply.Partscanbereplacedjust-before-needed,withservicelifecalculatedasafunctioninstalled-partquality,servicetime,andoperationalprofile—slashingthecostsofreplacing

perfectlysoundonafixedschedule.TraditionalMLalgorithmsoftenlieattheheartofpredictivemaintenance.Forexample,metrics

likeRemainingUsefulLife(RUL)HealthIndicatorscanidentifymotoranomalies.But

syntheticdatageneratedbyGANsandotherGenAIalgorithmscanimprovethealgorithms’

performance.MultipleMLtechniques,includingGenAIalgorithms,canbecombinedimprovefaultandpredictivemaintenanceworkflow19Forexample,techniquesbeenappliedacousticsignalsfrommachineryidentifyandpredictfaultsnotidentifiedbyothermethods.20

GANshavealsobeengeneratesyntheticmonitoring-datasetshelpotheralgorithmsimprovefailure-predictionandoptimizemaintenanceschedules.21GAN-FP,genetic

adversarialnetworksforfailureprediction,specializeingenerating,balancing,andtrainingdataimproveperformanceofotherMLalgorithms22.FaultmanagementOpportunity:Identifyfaults,makedynamicadjustments,andeffectasafelandingwhenrequired.18M.Ahn“DoICan,ISay:GroundingLanguageinRoboticAffordances.”arXiv,16,2022.Accessed:Mar.22024.[Online].Available:/abs/2204.0169119Z.Mianal.,“Aliteraturereviewoffaultensemblelearning,”EngineeringApplicationsofArtificialIntelligence,vol.Jan.2024,10.1016/j.engappai.2023.107357.Wang,VinogradovImprovingperformanceofconvolutionalhistory-stateensembleunsupervisedearlyfaultdetectionwithacousticemissionsignals

Sci.,(5)(2023),p.3136,10.3390/APP13053136H.Wang,J.Zhao,andX.“AMaintenance-predictionMethodforAircraftusingGenerativeAdversarialNetworks,”inIEEE5thInternational

onComputerandCommunications(ICCC),Dec.2019,pp.225–doi:10.1109/ICCC47050.2019.9064184.

Zheng,Farahat,andC.Gupta,“GenerativeAdversarialNetworksforFailurePrediction.”arXiv,04,2019.Accessed:Mar.15,2024.[Online].Available:

/abs/1910.02034GenAImodelscantransformdataforotherMLimprovefaultdetectioninautonomous

systems.Forexample,VAEscanhelpcompressoperationaldatarepresentationsforlongshort-termmemorynetworks(LSTN),atypeofrecurrentneural

netwo23Spatio-temporaltransformercancapturetrendsanddimensionsacross

differenttimescalesimprovebatterydiagnosisfailureprognosis,enhancingpredictive

maintenanceforUAVs.Forexample,thesystemcanspotsubtlechangesinvisibleearlierMLtechniques)thatsignalimpendingbatteryfailureasmuchas24hours

beforethebatteries.24GANshavebeenusedgeneratetrainingsamplesandbuildinferencenetworksforaircraft-

enginemonitoringimprovefailurepredictionsofotherMLalgorithms.25

ResearchershavecombinedVAEsandLSTMsupportcontinuouslearningfromvehiclesensor

data,generatingsyntheticdataforwiderrangesoffaultscenarios.BytrainingotherMLalgorithms

onthisofsyntheticdata,Sadhuetachievedaccuracyindetectingfaultsaccuracyinclassifyingthem.Demandsforcomputingpowerrelativelyslowexecutionspeedaretopconcernswhen

runningthesekindsofalgorithmsonlow-costhardware.Oneisportthecomputations

whicharemorepower-efficientthanGPUs.ThisishowSadhuetal.achievedaspeedup(athalfthepowerconsumption)forVAE-LSTMfaultdetectionalgorith26

VAEscanalsobeusedtrainmodelsthatidentifynormaloperation.Usingthistechnique,Dhakl

etal.achieveda95.6%accuracyindetectingdeviationsindicativeoffaultsandanomaliesrepresentedinthetrainingset.27Whenaproblemarisesinadroneoritscommunicationsnetwork,thedronemustlandsafelyavoidsecondarydamage.thisMonteCarloalgorithmshavebeencalculate“targetlevelsofsafety”(levelsofacceptablerisk)forvariouslandingzone28

Techniqueslikethiscouldbecombinedwithtransformersmakecontext-awaredecisionswhen

afaultforcesaUAVsystemselectanappropriatelandingzone.thefuture,itmayalsobepossibleGenAItechniquestransformersletself-healinresponsehardwarefailures,softwarebugs,ornetworkdisruption.Forexample,

Khlaisamniangetal.proposedaforusingdetectanomalies,generate

code,debugit,andcreatereportsoncomputersystems.29Althoughstillinitsearlystages,this

worksuggestsdirectionsforfutureresearchonotherautonomoussystems.23Han,A.L.Ellefsen,F.T.Holmeset,H.Zhang,“FaultDetectionWithLSTM-BasedVariationalAutoencoderMaritimeComponents,”IEEESensorsvol.19,pp.2190321912,2021,doi:10.1109/JSEN.2021.3105226.24J.Zhao,Feng,J.Wang,Lian,M.Ouyang,andF.Burke,"Batteryfaultdiagnosisandfailureprognosisforelectricvehiclesspatio-temporaltransformernetworks,"

AppliedEnergy,vol.25H.Wang,J.Zhao,andX.“AMaintenance-predictionMethodforAircraftusingGenerativeAdversarialNetworks,”inIEEE5thInternational

onComputerandCommunications(ICCC),Dec.2019,pp.225–doi:10.1109/ICCC47050.2019.9064184.26Sadhu,andD.Pompili,“On-BoardDeep-Learning-BasedUnmannedAerialFaultCauseDetectionandClassificationviaFPGAs,”IEEETransactionsvol.no.4,pp.33193331,2023,doi:10.1109/TRO.2023.3269380.27R.Dhakal,C.Bosma,Chaudhary,andN.Kandel,"UAVfaultandanomalydetectionusingautoencoders,"inProceedingsIEEE/AIAADigitalAvionicsSystems

Conference.IEEE,2023,pp.1-8.28Tong,Gan,L.Yu,andH.Zhang,“EvaluationTargetofUnmannedVehicleinFusionAirspace,”inIEEEInternationalConferenceArtificialIntelligenceandComputerApplications(ICAICA),Jun.pp.37510.1109/ICAICA54878.2022.9844489.29Khlaisamniang,P.Khomduean,Saetan,Wonglapsuwan,GenerativeAIforSelf-HealingSystems.2023,10.1109/iSAI-NLP60301.2023.10354608.FleetGenAIcanhelpimproveswarmintelligence,swarmcoordination,andtherobustness,security,

andefficiencyofunderlyingcommunicationsnetworksattheleveloforswarmsof

autonomousthings.thiscontext,ZeroTrustsecurity,safety,andresilienceintoplay—

protectingdronefleets,improvingtheintegritysharedsensing,facilitatingbettercoordination,

andreducingtheimpactofacompromiseddroneonthefleetasawhole.SwarmintelligenceOpportunity:Improvetrustworthyfusionofsensordatafrommultipleindividualsinaswarm.

Overthepastdecade,researcherspublishedtensofthousandsofpapersonsynthesizing

unifiedsituationalviewsbyfusingfromnumerousdronestogether.Supposethata

dronefleetissurveyingalarge-scaledisasterafloodorafire.Ideally,trustworthyswarm

intelligencecaninformationfrommemberoftheswarmpaintacomprehensive

pictureofthesituation.GenerativeKnowledge-SupportedTransformers(GKSTs),forexample,canfuseimageryfrom

differentviewsofatargetobject,producingmoremeaningfulimagesfromamovingvehicle.30

Furtherenhancementsofthismulti-viewapproachmightimprovetheinterpretationofcollectedfromthedifferingperspectivesofmembers.importantappreciatethattheremanywaysofrepresentingtheworld,different

approachesmaybesuitablefordifferentpurposes.Forexample,similarityclassification

algorithmscanhelptheobjectfeaturesinimagery.contrast,semantic

categorizationalgorithmslabeltheseasmembersofspecificcategoriesorclasses.

SA-SIAM(atwo-foldSemantic-Appearanceneuralnetwork)sharesinformationacross30Yu,W.Liao,C.Qu,Q.andZ.Xu,“UAVCooperativeSearchMulti-agentGenerativeAdversarialImitationin2022InternationalConferenceon

Learning,CloudComputingandIntelligentMining(MLCCIM),Aug.pp.441446.doi:10.1109/MLCCIM55934.2022.00081.separateneuralnetworks,onetrainedonSemanticinformation,theotheronAppearancedata.31

TheSemanticsideusesattentionmechanismhelpinterpretdatabasedonthetargetadditionalcontextualinformation.Althoughthisnotafull-generativeAIimplementation,itshows

howamoretargetedattentionmechanismintransformerscanbeappliedotherusecasesina

moretargetedway,whichmaybeefficientthanafull-blownLLMimplementation.ConditionalGANs(CGANs)—variantsofGenerativeAdversarialNetworksbasedspecific

conditions—beenusedformotionprediction.Thesepredictionsconsidereachobject's

relativemotionitschangingorientationrelativeaUAV.32Theseworkinconjunctiona

Siamesenetwork,inwhichneuralaresharedacrossapairofcomplementarynetworks.Thoughtheinitialresearchfocusedonindividualdrones,theworksuggestsafuture

pathforsynthesizing3Dviewsofsituationscontributionsacrossafleet.2016,researchersexploreda“socialpooling”layercouldhelpautonomousagentsmodel

theinteractionsofpeopleproximity,usingseparateLSTMnetworkspredictperson’s

motion33thiscase,theresearcherswerelookingatanautonomouscouldplanitspaththroughgroupsofindependentlymovinghumanbeings.Futureresearchexplorehowsocialpoolingcouldextendimprovemodelsthatallowdronesunderstand

currentlocationsandpredictfuturepositionsofnearbydrones,bystanderdrones,andoutsideradversarydrones.Text-to-image-baseddiffusionmodelsalsobeenusedgeneraterealisticimagesofUAVs

invaryingscenarios,improvingalgorithmsfordetectingUAVsby12%.Theresearcherscombined

normalizedmod