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基于分段核函数的SVM入侵检测方法.docx

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基于分段核函数的SVM入侵检测方法 Abstract Intrusiondetectionisanimportantaspectinthefieldofnetworksecurity,whichhelpsintheidentificationofpotentialsecuritythreatsandharmfulactivitiesinanetwork.Inthispaper,weproposedasupportvectormachine(SVM)basedintrusiondetectionsystemusingasegmentedkernelfunction.Thesystemactsasabinaryclassifierthateffectivelyseparatesnormalnetworktrafficfrommaliciousactivities.ThesegmentedkernelfunctionhelpsinimprovingtheclassificationaccuracyoftheSVMmodel.Experimentalresultsobtainedindicatethattheproposedsystemishighlyefficientandoutperformsexistingintrusiondetectionsystems. Introduction Intrusiondetectionisanimportantaspectofnetworksecuritythathelpsindetectingpotentialsecuritythreatsandharmfulactivitiesinanetwork.Thisinvolvesthemonitoringofnetworktrafficandtheidentificationofactivitiesthatdeviatefromnormalbehavior.Intrusiondetectionsystems(IDS)areusedtodetectnetworkattacks,suchasdenial-of-service(DoS)attacks,bufferoverflowattacks,andothers,andtheyarecommonlyusedindifferenttypesofnetworks,includingLANs,WANs,andtheinternet. Intrusiondetectionsystemscanbeclassifiedintotwocategories:signature-basedandanomaly-based.Signature-basedintrusiondetectionsystemsusepredefinedattacksignaturestoidentifythreats,whileanomaly-basedintrusiondetectionsystemsrelyonstatisticalormachinelearningtechniquestoidentifyabnormalnetworkactivity.Machinelearningapproaches,suchasSVM,havebeenshowntobeeffectiveinintrusiondetectionduetotheirabilitytohandlelargeamountsofdataandtheirabilitytolearnfromexamples. Inthispaper,weproposedasupportvectormachine(SVM)basedintrusiondetectionsystemusingasegmentedkernelfunction.TheSVMmodelactsasabinaryclassifierthateffectivelyseparatesnormalnetworktrafficfrommaliciousactivities.ThesegmentedkernelfunctionhelpsinimprovingtheclassificationaccuracyoftheSVMmodel. RelatedWork Machinelearning-basedintrusiondetectionsystemshavebeenwidelystudiedinrecentyears.SVM,inparticular,hasbeenshowntobeeffectiveinintrusiondetectionduetoitsabilitytohandlelargeamountsofdataanditsabi