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Predictive data mining for phishing websites: a rule based approach

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dc.contributor.author Fathima Shafana, Abdul Raheem
dc.contributor.author Fathima Shihnas Fanoon, Abdul Raheem
dc.date.accessioned 2021-04-01T09:25:53Z
dc.date.available 2021-04-01T09:25:53Z
dc.date.issued 2020-09-18
dc.identifier.citation Journal of Information Systems & Information Technology Vol. 5 No. 2, 2020 pp. 61-71. en_US
dc.identifier.issn 24780677
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/5425
dc.description.abstract The rapid advancement in internet has paved way for several serious crimes, of which phishing occupies a very important place. Phishing is a form of cybercrime where an attacker mimicking a legitimate website or a person or an organization redirects the victims to steal confidential data through e-mail, malwares or some other social engineering platforms. Victims prominently suffer from financial loss and private data loss. The serious outbreak of phishing has paved way for many researches, though comprehensive and accurate solution has not been proposed so far for thwarting its impact. This paper aims to develop a resilient model to predict phishing scam by means of classification algorithms of data mining. Five algorithms were chosen for this purpose and a comparative study was undertaken for their performances, accuracy, error rate and efficiency. The rules generated from the algorithms showed up a relatively better performance than the existing phishing detection tools en_US
dc.language.iso en_US en_US
dc.publisher © Faculty of Management and Commerce South Eastern University of Sri Lanka en_US
dc.subject Phishing en_US
dc.subject Data Mining en_US
dc.subject Classification en_US
dc.subject PART en_US
dc.subject Website Legitimacy en_US
dc.title Predictive data mining for phishing websites: a rule based approach en_US
dc.type Article en_US


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