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Volume 5 , Issue 2 , December 2021 , Pages: 66 - 75
An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier
Adetokunbo MacGregor John-Otumu, Department of Computer Science, Morgan State University, Baltimore, USA
Md Mahmudur Rahman, Department of Computer Science, Morgan State University, Baltimore, USA
Christiana Ugochinyere Oko, Department of Information Technology, Federal University of Technology, Owerri, Nigeria
Received: Oct. 12, 2021;       Accepted: Nov. 1, 2021;       Published: Nov. 5, 2021
DOI: 10.11648/j.ajai.20210502.13        View        Downloads  
Abstract
An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.
Keywords
Phishing, Machine Learning, Random Forest, Web Browsers, Web Sites
To cite this article
Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko, An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier, American Journal of Artificial Intelligence. Vol. 5, No. 2, 2021, pp. 66-75. doi: 10.11648/j.ajai.20210502.13
Copyright
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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