uchilecl Richard Weber Sebasti an R 305os Department of Industrial Engineering University of Chile Rep ublica 701 Santiago Chile Email rwebersrios diiuchilecl Abstract Phishing email fraud has been considered as one of the main cyberthreats over the ID: 7427
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ORKNowadays,inthecyber-crime keepraisingastheinternetpenetrationinoureverydaylifeincreases.DifferenttextminingtechniquesforphishingÞlteringhavebeenproposed.In[1],LogisticRegression,SupportVectorMachines(SVMs),andRandomForestsareusedtoestimateclassiÞersforthecorrectlabelingofemailmessages.Byusingofmoresophisticatedtextminingtechniques,Bergholzetal.([3],[4])proposedanovelcharacterizationofemailsusingaClass-Topicmodel.Forphishingfeatureextractionseveralmethodologieshavebeendeveloped[1],[2],[4],[7],while combinationofstructuralbasicfeatures$,whichareinde-pendentfromtheothercontentbasedfeaturesset#,!and".However,thesesetsarenotindependentfromeachother.Theyarerepresentedbybinaryfeatures,indicatingwhetherakeywordortopicispresentedinagivenmessage,whoseintersectiondescribesaÞnalsetoffeaturesthatrepresentsa tiveclassiÞermodelsrepresentedbylogisticregression.ThissupportstheussualpreferenceofSVMsforclassiÞcationtasks,speciallyintext-miningapplications. GerhardPaass,andSiehyunStrobel.NewÞlteringapproachesforphishingemail.