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dc.contributor.authorAksu, Doğukan
dc.contributor.authorTurgut, Zeynep
dc.contributor.authorÜstebay, Serpil
dc.contributor.authorAydın, Muhammed Ali
dc.date.accessioned2020-02-14T13:16:39Z
dc.date.available2020-02-14T13:16:39Z
dc.date.issued2019
dc.identifier504tr_TR
dc.identifier.issn1876-1100
dc.identifier.urihttps://hdl.handle.net/11413/6227
dc.description.abstractIn today's world, where all records are carried into an electronic environment, cyber security represents a very broad scope, with the primary objective of preventing the loss of financial and/or emotional loss of people, institutions, organizations through the security of data in the digital environment. Today, the most common cyber security threat is phishing attacks. With the phishing attack, the attacker aims to capture the data which are very important for the individuals like identification number, social security number, bank account information, and so on. In this study, using deep learning, it was checked whether the web sites are real or not by using neural networks and support vector machine, decision tree and stacked autoencoders as classification methods. As a result of the study, 86% success rate was reached by using stacked autoencoders which are a part of deep learning techniques.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectPhishing
dc.subjectStacked Autoencoders
dc.subjectClassification
dc.subjectSupport Vector Machine
dc.subjectDecision Trees
dc.subjectE-dolandırıcılık
dc.subjectYığılmış Otomatik Kodlayıcılar
dc.subjectSınıflandırma
dc.subjectDestek Vektör Makinesi
dc.subjectKarar Ağaçları
dc.titlePhishing Analysis of Websites Using Classification Techniques
dc.typeconferenceObjecttr_TR
dc.relation.journalInternational Telecommunications Conferance, Itelcon 2017tr_TR
local.journal.startpage251tr_TR
local.journal.endpage258tr_TR
dc.identifier.scopus2-s2.0-85049985643


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States