Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3839
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dc.contributor.authorAydın, M. Akif-
dc.contributor.authorBütün, İsmail-
dc.contributor.authorBıçakcı, Kemal-
dc.contributor.authorBaykal, N.-
dc.date.accessioned2020-10-22T16:40:33Z-
dc.date.available2020-10-22T16:40:33Z-
dc.date.issued2020-01
dc.identifier.citationAydin, M., Butun, I., Bicakci, K. and Baykal, N. (2020, January). Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0774-0779). IEEE.en_US
dc.identifier.isbn978-172813783-4
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3839-
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9031125-
dc.description.abstractPhishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all. © 2020 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAttribute-based feature selectionen_US
dc.subjectcyber theften_US
dc.subjectdata analysisen_US
dc.subjectfraudulent website detectionen_US
dc.subjectmachine learning algorithmsen_US
dc.titleUsing Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLsen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage774
dc.identifier.endpage779
dc.relation.ecSwedish Foundation for International Cooperation in Research and Higher Education [IB2019-8185]en_US
dc.relation.ecHorizon 2020 Framework Programme [773717]en_US
dc.authorid0000-0002-2378-8027-
dc.identifier.wosWOS:000668567200121en_US
dc.identifier.scopus2-s2.0-85083083149en_US
dc.institutionauthorBıçakcı, Kemal-
dc.identifier.doi10.1109/CCWC47524.2020.9031125-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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