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Grid环境下基于实体行为的信任评估模型.docx

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Grid环境下基于实体行为的信任评估模型 1.Introduction Inrecentyears,trustevaluationhasbeenahottopicinthefieldofgridcomputing.Withtheincreasingparticipationofheterogeneousentitiesingridenvironments,trustevaluationhasbecomeakeyissueinensuringsecureandreliableresourcesharing.Ingridenvironments,trustevaluationcanbebasedonvariousfactorssuchasentityreputation,entitycompetency,entitybehavior,andsoon.Inthispaper,weproposeatrustevaluationmodelbasedonentitybehaviorforgridenvironments. 2.RelatedWork Alotofresearchhasbeendoneintheareaoftrustevaluationingridenvironments.Earlyresearchfocusedontrustevaluationbasedonentityreputation,wherethereputationofanentityisdeterminedbasedonfeedbackfromotherentities.Thisapproachworkswellinsmallandtightlyknitcommunities,whereentitieshavealotofinteractionwitheachother.However,inlargeandopencommunitiessuchasgridenvironments,entityreputationmaynotbeenoughtoestablishtrust. Morerecentresearchhasfocusedontrustevaluationbasedonentitybehavior.Thisapproachtakesintoaccountthebehaviorofanentityinvarioussituations,suchasduringresourceallocation,resourceusage,andsoon.Entitybehaviorcanbeanalyzedusingvariousmachinelearningtechniquessuchasdecisiontrees,neuralnetworks,andBayesiannetworks. 3.TrustEvaluationModel Ourproposedtrustevaluationmodelisbasedonentitybehaviorandconsistsofthefollowingsteps: Step1:DataCollection Dataiscollectedfromvarioussourcessuchaslogs,audittrails,andsensordata.Thedatacollectedincludesinformationaboutresourceallocation,resourceusage,communicationpatterns,andsoon. Step2:FeatureExtraction Featuresareextractedfromthecollecteddatausingvarioustechniquessuchasstatisticalanalysis,datamining,andmachinelearning.Thefeaturesextractedincludethefrequencyofresourceusage,thedurationofresourceusage,theamountofdatatransferred,andsoon. Step3:BehaviorModeling Behaviormodelingisdoneusingvariousmachinelearningtechniquessuchasdecisiontrees,neuralnetworks,andBayesiannetworks.Thebehaviormodelsareusedtopredictthebehaviorofanentityinvarioussituations,suchasduringresourceallocation