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1、DEFENDINGAGAINSTMALICIOUSNODESUSINGANSVMBASEDREPUTATIONSYSTEMRehanAkbani,TurgayKorkmaz,andG.V.S.Raju{rakbani@cs.utsa.edu,korkmaz@cs.utsa.edu,andgvs.raju@utsa.edu}UniversityofTexasatSanAntonioSanAntonio,TX,USAABSTRACTfuturebehaviorofanodebyanalyzingitspastbehavior.Ea
2、chnodethatinteractswithanothernodestoressomeManynetworks,suchasP2Pnetworks,MANETs,filefeedbackabouttheinteraction.Theinteractioniseithersharingnetworks,andonlineauctionnetworksrelyonclassifiedaslegitimateorsuspicious(orsomevalueinnodecooperation.Ifamaliciousnodegain
3、saccesstosuchbetween).If,forinstance,theinteractionconsistedofanetworkitcaneasilylaunchattacks,suchasspreadingdownloadingafile,theclientcoulddetermineifthevirusesorspam,orattackingknownvulnerabilities.downloadedfilewasindeedtheonerequested,oritwasaReputationSystems(
4、RS)defendagainstmaliciousnodesTrojan,avirus,orspam.Basedonthefeedbackofvariousbyobservingtheirpastbehaviorinordertopredicttheirnodes,anewnodecandecidewhethertotransactwithafuturebehavior.TheseRSsusuallycompriseofstatisticalgivennodeornot,eventhoughtheymayneverhavemo
5、delsorequationsthataredesignedbyhandandonlyinteractedbefore.eBayutilizesthisformofreputationdefendagainstspecificpatternsofattacks.Inthispaper,systemwhereusersleavefeedbackaboutotherusers.weproposeaSupportVectorMachines(SVM)basedRSthatdefendsagainstmanypatternsofatt
6、acks.ItcanbeIngeneral,wecansummarizeexistingRSs[1-3,5]withinretrainedtodetectnewattackpatternsaswell.WediscussageneralframeworkdepictedinFig1.AccordingtothisthechallengesassociatedwithbuildingRSsandhowourframework,anodethatneedstodecidewhethertotransactRStackleseach
7、ofthem.WecomparetheperformanceofwithanothernodeornotmustfirstgatherhistoricaldataourRSwithanotherRSfoundintheliterature,calledaboutthatnode(e.g.,theproportionofgoodvs.badTrustGuard,andperformdetailedevaluationsagainstatransactionsinthelastxminutes).Thenitappliesavar
8、ietyofattacks.TheresultsshowthatourRScustomizedmathematicalequationorstatisticalmodeltosignificantlyoutperformsTrustGuard.Evenwhenthetheda
