Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/4257
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dc.contributor.authorHanees, A. L.-
dc.date.accessioned2020-01-09T03:56:38Z-
dc.date.available2020-01-09T03:56:38Z-
dc.date.issued2019-12-18-
dc.identifier.citation8th South Eastern University International Arts Research Symposium -2019. 18th December 2019. South Eastern University of Sri Lanka, Oluvil, Sri Lanka. pp. 104.en_US
dc.identifier.isbn978-955-627-203-1-
dc.identifier.urihttp://ir.lib.seu.ac.lk/handle/123456789/4257-
dc.description.abstractPhishing is a type of social engineering attack often used to steal user data. Especially in e-banking attackers sent an email that appears legitimate but is actually meant to lure a potential victim into providing some level of personal information for nefarious purpose, including login credentials and credit card numbers. In this paper we employ four data mining classification algorithms to detect the phishing emails in e-banking and then we use the weighted majority vote ensemble method to improve the detection of phishing emails and compare the performance of each. Experimental results shows that decision tree builds the best classifier.en_US
dc.language.isoen_USen_US
dc.publisherFaculty of Arts and Culture South Eastern University of Sri Lanka.en_US
dc.subjectPhishing emailsen_US
dc.subjectE-Bankingen_US
dc.subjectEnsembleen_US
dc.subjectWeighted majority voteen_US
dc.subjectData mining algorithmen_US
dc.titlePhishing e mail detection in e Banking using data mining techniquesen_US
dc.typeArticleen_US
Appears in Collections:SEUIARS - 2019

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