Phishing Web Sites Features Classification Based on Extreme Learning Machine

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dc.contributor.authorSonmez, Yasin
dc.contributor.authorTuncer, Turker
dc.contributor.authorGokal, Huseyin
dc.contributor.authorAvci, Engin
dc.date.accessioned2024-04-24T17:37:45Z
dc.date.available2024-04-24T17:37:45Z
dc.date.issued2018
dc.departmentDicle Üniversitesien_US
dc.description6th International Symposium on Digital Forensic and Security (ISDFS) -- MAR 22-25, 2018 -- Antalya, TURKEYen_US
dc.description.abstractPhishing are one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. The purpose of this study is to perform Extreme Learning Machine (ELM) based classification for 30 features including Phishing Websites Data in UC Irvine Machine Learning Repository database. For results assessment, ELM was compared with other machine learning methods such as Support Vector Machine (SVM), Naive Bayes (NB) and detected to have the highest accuracy of 95.34%en_US
dc.description.sponsorshipIEEE Turkey Sect,Firat Univ,Sam Houston State Univ,Gazi Univ,Univ Arkanas Little Rock,Polytechn Inst Cavado & Ave,Havelsan,Balikesir Univ,Hacettepe Univ,Youngstown State Univ,Baskent Univ,Petru Maior Univen_US
dc.identifier.endpage159en_US
dc.identifier.isbn978-1-5386-3449-3
dc.identifier.scopus2-s2.0-85050966207
dc.identifier.scopusqualityN/A
dc.identifier.startpage155en_US
dc.identifier.urihttps://hdl.handle.net/11468/21162
dc.identifier.wosWOS:000434247400029
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 6th International Symposium on Digital Forensic and Security (Isdfs)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtreme Learning Machineen_US
dc.subjectFeatures Classificationen_US
dc.subjectInformation Securityen_US
dc.subjectPhishingen_US
dc.titlePhishing Web Sites Features Classification Based on Extreme Learning Machineen_US
dc.titlePhishing Web Sites Features Classification Based on Extreme Learning Machine
dc.typeConference Objecten_US

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