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基于文本聚类的网络攻击检测方法.docx

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基于文本聚类的网络攻击检测方法 Title:NetworkAttackDetectionusingTextClustering Abstract: Withtheincreasingcomplexityandscaleofcomputernetworks,thenumberandsophisticationofnetworkattacksarealsogrowingrapidly.Traditionalnetworksecuritymethods,suchassignature-baseddetectionandanomalydetection,areinsufficienttoeffectivelydetectandmitigatetheseattacks.Thispaperproposesanovelapproachfornetworkattackdetectionusingtextclusteringtechniques.Byanalyzingnetworktrafficlogsandextractingimportanttextualfeatures,thismethodaimstodetectandclassifynetworkattacksbasedontheirtextualcharacteristics.Theproposedapproachisexpectedtoenhancetheaccuracyandefficiencyofnetworkattackdetection,therebyimprovingoverallnetworksecurity. 1.Introduction Networkattacksposeaseriousthreattothesecurityandstabilityofcomputernetworks.Traditionalnetworksecuritymethodsoftenrelyonpredefinedsignaturesordeviationsfromnormalbehaviors.However,thesemethodsarelimitedintheirabilitytodetectunknownorevolvingattacks.Therefore,thereisaneedforinnovativeapproachesthatcaneffectivelydetectandclassifynetworkattacksbasedontheirtextualpatterns.Thispaperpresentsatextclustering-basedmethodfornetworkattackdetection. 2.RelatedWork Thissectionprovidesanoverviewofexistingnetworkattackdetectiontechniques,includingsignature-basedandanomaly-basedapproaches.Itdiscussestheirlimitationsandmotivatestheneedforanovelapproachusingtextclusteringalgorithms. 3.Methodology Theproposedmethodconsistsofthefollowingsteps: a)DataPreprocessing:Networktrafficlogsarepreprocessedtoextracttextualfeaturesforeachnetworksession. b)FeatureExtraction:Relevantfeaturesareextractedfromthepreprocesseddatausingtechniquessuchastermfrequency-inversedocumentfrequency(TF-IDF)andwordembeddings. c)TextClustering:Theextractedfeaturesareclusteredusingappropriatetextclusteringalgorithms,suchasK-meansorhierarchicalclustering. d)AttackDetection:Thetextclustersareanalyzedtoidentifyclustersthatrepresentnetworkattacks.Variousmetrics,suchasentropyormutualinformation,canbeusedtomeasurethesignificanc