Performance Evaluation of Machine Learning Tools for Detection of Phishing Attacks on Web pages

dc.contributor.author	Oguntunde, Bosede
dc.date.accessioned	2022-10-12T09:41:50Z
dc.date.available	2022-10-12T09:41:50Z
dc.date.issued	2022-03
dc.description.abstract	This paper analyses and implements a rule-based approach for phishing detcetion using the three machine learning models trained on a dataset consisting of fourteen (14) features. The machine learning algorithms are: K-Nearest Neighbour (KNN). random Forest and Support vector MAchine (SVM). Among the three algorithm 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 extension called PhishNet. PhishNet is build during the course of this research using web technologies such as HTML, CSS, and Javascript. As a result, PhishNet facilities highly efficient phishing detection for the web.	en_US
dc.identifier.uri	http://dspace.run.edu.ng:8080/jspui/handle/123456789/3712
dc.language.iso	en	en_US
dc.publisher	Scientific African	en_US
dc.relation.ispartofseries	Vol. 16;
dc.subject	Phishing	en_US
dc.subject	Attack	en_US
dc.subject	KNN	en_US
dc.subject	Random Forest	en_US
dc.subject	SVM	en_US
dc.title	Performance Evaluation of Machine Learning Tools for Detection of Phishing Attacks on Web pages	en_US
dc.type	Article	en_US

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