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dc.contributor.authorŞahingöz, Özgür Koray
dc.contributor.authorBuber, Ebubekir
dc.contributor.authorDemir, Önder
dc.contributor.authorDiri, Banu
dc.date.accessioned2019-09-05T12:31:17Z
dc.date.available2019-09-05T12:31:17Z
dc.date.issued2017-12
dc.identifier.urihttps://hdl.handle.net/11413/5244
dc.description.abstractDue to the rapid growth of the Internet, users change their preference from traditional shopping to the electronic commerce. Instead of bank/shop robbery, nowadays, criminals try to find their victims in the cyberspace with some specific tricks. By using the anonymous structure of the Internet, attackers set out new techniques, such as phishing, to deceive victims with the use of false websites to collect their sensitive information such as account IDs, usernames, passwords, etc. Understanding whether a web page is legitimate or phishing is a very challenging problem, due to its semantics-based attack struc­ ture, which mainly exploits the computer users’ vulnerabilities. Although software companies launch new anti-phishing products, which use blacklists, heuristics, visual and machine learning-based approaches, these products cannot prevent all of the phishing attacks. In this paper, a real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed. The system has the following distinguishing properties from other studies in the literature: language independence, use of a huge size of phishing and legitimate data, real-time execution, detection of new websites, independence from third-party services and use of feature-rich classifiers. For mea­ suring the performance of the system, a new dataset is constructed, and the experimental results are tested on it. According to the experimental and comparative results from the implemented classification algorithms, Random Forest algorithm with only NLP based features gives the best performance with the 97.98% accuracy rate for detection of phishing URLs.tr_TR
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.subjectCyber Securitytr_TR
dc.subjectPhishing Attacktr_TR
dc.subjectMachine Learningtr_TR
dc.subjectClassification Algorithmstr_TR
dc.subjectCyber Attack Detectiontr_TR
dc.subjectSiber Güvenliktr_TR
dc.subjectKimlik Avı Saldırısıtr_TR
dc.subjectMakine Öğrenmetr_TR
dc.subjectSınıflandırma Algoritmalarıtr_TR
dc.subjectSiber Saldırı Tespititr_TR
dc.titleMachine Learning Based Phishing Detection from URIstr_TR
dc.typeArticletr_TR
dc.contributor.authorID214903tr_TR
dc.relation.journal17. International Conference on Intellegent Systems Design and Applicationstr_TR


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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