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http://ir.lib.seu.ac.lk/handle/123456789/4257
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DC Field | Value | Language |
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dc.contributor.author | Hanees, A. L. | - |
dc.date.accessioned | 2020-01-09T03:56:38Z | - |
dc.date.available | 2020-01-09T03:56:38Z | - |
dc.date.issued | 2019-12-18 | - |
dc.identifier.citation | 8th 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.isbn | 978-955-627-203-1 | - |
dc.identifier.uri | http://ir.lib.seu.ac.lk/handle/123456789/4257 | - |
dc.description.abstract | Phishing 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.iso | en_US | en_US |
dc.publisher | Faculty of Arts and Culture South Eastern University of Sri Lanka. | en_US |
dc.subject | Phishing emails | en_US |
dc.subject | E-Banking | en_US |
dc.subject | Ensemble | en_US |
dc.subject | Weighted majority vote | en_US |
dc.subject | Data mining algorithm | en_US |
dc.title | Phishing e mail detection in e Banking using data mining techniques | en_US |
dc.type | Article | en_US |
Appears in Collections: | SEUIARS - 2019 |
Files in This Item:
File | Description | Size | Format | |
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Abstract_book_SEUIARS2019 - Page 129.pdf | 82.48 kB | Adobe PDF | View/Open | |
Full Paper 142.pdf | 861.73 kB | Adobe PDF | View/Open |
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