A maximum entropy classification scheme for phishing detection using parsimonious features

dc.contributor.authorBello, Auwal Ahmed
dc.contributor.authorChiroma, Haruna
dc.contributor.authorGital, Abdulsalam Ya’u
dc.date.accessioned2023-09-04T13:00:53Z
dc.date.available2023-09-04T13:00:53Z
dc.date.issued2021-06-13
dc.description.abstractOver the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM)en_US
dc.description.sponsorshipACE: Technology Enhanced Learningen_US
dc.identifier.issn1693-6930,
dc.identifier.urihttp://hdl.handle.net/123456789/2119
dc.language.isoenen_US
dc.publisherTELKOMNIKA Telecommunication, Computing, Electronics and Controlen_US
dc.relation.ispartofseriesTELKOMNIKA Telecommunication, Computing, Electronics and Control;1693-6930
dc.subjectClassificationen_US
dc.subjectClassificationen_US
dc.subjectPhishingen_US
dc.subjectSocial engineeringen_US
dc.subjectNigeriaen_US
dc.subjectDigital Developmenten_US
dc.subjectACE: Technology Enhanced Learningen_US
dc.subjectACETELen_US
dc.titleA maximum entropy classification scheme for phishing detection using parsimonious featuresen_US
dc.typeArticleen_US
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