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基于CNN与LightGBM的入侵检测研究.docx

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基于CNN与LightGBM的入侵检测研究 Introduction WiththerapiddevelopmentofnetworktechnologiesandtheincreasingusageoftheInternet,theissueofcybersecurityhascaughtglobalattention.Thenumberofcyberattacks,includingnetworkintrusion,hasincreasedremarkably,whichposesaseverethreattothesecurityofinformationsystems.Totacklethisissue,variousmethodsandtechniqueshavebeenproposedfordetectingandpreventingnetworkintrusion.ThispaperfocusesontheresearchofintrusiondetectionusingacombinationofConvolutionalNeuralNetwork(CNN)andLightGBM. Background Intrusiondetectionistheprocessofdiscoveringabnormalormaliciousactivitiesinacomputersystem.Previouslyproposedmethodsofintrusiondetectionmainlyinvolvedtheuseofrules,signatures,orexpertknowledge.However,thesetraditionalmethodsarelimitedbytheirrelianceonthepriorknowledgeofknownintrusionpatterns,andtheymaymissnewandunknownmaliciousactivities.Therefore,machinelearningapproacheshavebeenincreasinglyappliedtointrusiondetectionsincetheycanadapttodetectnewintrusionpatterns. ConvolutionalNeuralNetwork(CNN)isapowerfulmachinelearningtechniquethatcanautomaticallylearnpatternsandfeaturesfromrawdata.CNNhasachievedgreatresultsinvariousresearchfields,includingimagerecognition,speechprocessing,andnaturallanguageprocessing.Asnetworkdatacanberepresentedasasequenceofnumericalvalues,CNNcanalsobeutilizedtoanalyzeandclassifynetworktrafficdataforintrusiondetection. LightGBMisagradientboostingframeworkthatcanbeusedforclassificationandothermachinelearningtasks.LightGBMcanefficientlyworkwithlarge-scaledatasetsandhasbeenwidelyusedinvariousmachinelearningapplications.LightGBMisasuitablemachinelearningalgorithmforintrusiondetection,asitprovidesanoptimizedperformanceforclassificationtasks. Methodology TheproposedmethodofintrusiondetectionusingacombinationofCNNandLightGBMconsistsoffourmainphases:datapreprocessing,featureextraction,training,andtest.Thedetailedprocedureisasfollows: DataPreprocessing: Thefirststepistopreprocessthedatatomakeitsuitableformachinelearningalgorithms.Thedataiscleaned,