Performance evaluation of machine learning tools for detection of phishing attacks on web pages

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dc.contributor.authorOjewumi TO
dc.contributor.authorOgunleye GO
dc.contributor.authorOguntunde BO
dc.contributor.authorFolorunsho O
dc.contributor.authorFashoto SG
dc.contributor.authorOgbu N
dc.date.accessioned2022-07-27T16:38:37Z
dc.date.available2022-07-27T16:38:37Z
dc.date.issued2022
dc.descriptionScientific African
dc.description.abstractThis paper analyses and implements a rule-based approach for phishing detection using the three machine learning models trained on a dataset consisting of fourteen (14) features. The machine learning algorithms are; k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). Among the three algorithms used, it was discovered that Random Forest model proved to deliver the best performance. Rules were extracted from the Random Forest Model and embedded into a Google chrome browser extension called PhishNet. PhishNet is built during the course of this research using web technologies such as HTML, CSS, and Javascript. As a result, PhishNet facilitates highly efficient phishing detection for the web.
dc.identifier.citation10.1016/j.sciaf.2022.e01165
dc.identifier.issn2468-2276
dc.identifier.urihttps://nerd.ethesis.ng/handle/123456789/375
dc.language.isoen
dc.subjectPhishing
dc.subjectAttack
dc.subjectKNN
dc.subjectRandom Forest
dc.subjectSVM
dc.titlePerformance evaluation of machine learning tools for detection of phishing attacks on web pages
dc.typeArticle
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