Securing the web: Machine learning's role in predicting and preventing phishing attacks

Md Kamrul Hasan Chy *

Department of Computer Information System and Analytics, University of Central Arkansas, 201 Donaghey Ave, Conway, Arkansas, USA.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 1004–1011.
Article DOI: 10.30574/ijsra.2024.13.1.1770
Publication history: 
Received on 11 August 2024; revised on 22 September 2024; accepted on 24 September 2024
 
Abstract: 
Website phishing is an evolving threat that poses significant risks to online users and organizations. This paper explores the application of machine learning techniques to detect phishing websites by analyzing key features such as URL structure, domain registration, and SSL/TLS certificates. Machine learning offers a dynamic, real-time approach to identifying phishing websites, providing enhanced accuracy and adaptability compared to traditional detection methods. By leveraging the ability of machine learning models to continuously learn and adapt to new phishing strategies, this research highlights the potential of these techniques to effectively combat emerging phishing threats. The paper also discusses future innovations, such as deep learning and natural language processing, which hold promise for further improving phishing detection systems and strengthening overall web security. These findings underscore the critical role of machine learning in safeguarding users from the growing threat of phishing websites.
 
Keywords: 
Phishing Website; Fraud Detection; Machine Learning; Machine Learning Algorithm; Anomaly Detection; Phishing Detection
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0