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Phishing e-mail detection by using deep learning algorithms

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dc.contributor.author Hassanpour, Reza
dc.contributor.author Doğdu, Erdoğan
dc.contributor.author Choupani, Roya
dc.contributor.author Göker, Onur
dc.date.accessioned 2020-11-30T11:26:27Z
dc.date.available 2020-11-30T11:26:27Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/20.500.12416/4269
dc.description.abstract Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users’ vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1145/3190645.3190719 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Machine Learning tr_TR
dc.subject Deep Learning tr_TR
dc.subject Supervised Learning tr_TR
dc.subject Classification tr_TR
dc.subject Malware Detection tr_TR
dc.title Phishing e-mail detection by using deep learning algorithms tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Proc. of the ACMSE 2018 Conference tr_TR
dc.contributor.authorID 21259 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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