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基于XGBoost算法的异常用户识别（英文）.docx

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基于XGBoost算法的异常用户识别（英文） Title:AnomalyUserDetectionbasedonXGBoostAlgorithm Abstract: Withtherapidgrowthofonlineplatformsanddigitalservices,theneedforearlyidentificationandmitigationofabnormaluserbehaviorshasbecomecrucialforensuringthesecurityandintegrityofthesesystems.ThispaperproposesanovelapproachforanomalyuserdetectionusingthepowerfulXGBoostalgorithm.XGBoostisapopularmachinelearningtechniqueknownforitsexceptionalperformanceinsolvingclassificationproblems,makingitwell-suitedforabnormaluseridentification.TheproposedapproachharnessesthepowerofXGBoosttoaccuratelyandefficientlydetectanomaliesinuserbehaviorpatterns,thusenhancingtheoverallsecurityofonlineplatforms. 1.Introduction Theemergenceofonlineplatformsanddigitalserviceshasrevolutionizedvariousindustriessuchase-commerce,healthcare,andfinance.However,thisincreasingrelianceondigitaltechnologyalsoexposestheseplatformstovarioussecuritythreats,includinganomaloususerbehaviorssuchasfraudulentactivities,spamming,ormaliciousintent.Detectingsuchanomaliesiscriticalforprotectingsystems,ensuringdataintegrity,andmaintainingusertrust.Inthispaper,weproposeamethodologyforidentifyingabnormaluserbehaviorsusingtheXGBoostalgorithm. 2.Background 2.1AnomalyDetection Anomalydetectionreferstotheprocessofidentifyingpatternsorinstancesthatsignificantlydeviatefromthenormalbehaviorofasystem.Traditionalapproachestoanomalydetectionincludestatisticaltechniques,clusteringalgorithms,andrule-basedmethods.However,thesemethodsoftenstrugglewithcomplexanddynamicdatasets,makingthemlesssuitableforhandlinglarge-scaleuserbehaviordata.Machinelearningalgorithmsofferapromisingsolutiontothisproblembyleveragingtheirabilitytoautomatepatternidentification. 2.2XGBoostAlgorithm XGBoostisanadvancedgradientboostingmachinelearningalgorithmthathasgainedsignificantpopularityduetoitsexcellentpredictiveperformanceandscalability.Itutilizesacombinationofdecisiontreestocreateanensemblemodelthatdeliversaccuratepredictionsonawiderangeofclassificationproblems.XGBoostoptimizesboth