Performance Evaluation of Machine Learning Tools for Detection of Phishing Attacks on Web pages
Loading...
Date
2022-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific African
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.
Description
Keywords
Phishing, Attack, KNN, Random Forest, SVM