Secure Browse: AI-Powered Phishing Defense for Browsers

Santosh Kumar Kande ()

International Journal of Computing and Engineering, 2024, vol. 5, issue 4, 56 - 63

Abstract: Purpose: With the rising threat of phishing attacks exploiting user naivety, this report introduces a novel approach to bolster web security. Traditional rule-based systems and existing solutions fall short in addressing sophisticated phishing attempts. The proposed solution entails a Chromium-based browser extension that leverages machine learning classification techniques. Methodology: A Python web server, utilizing decision trees, k- nearest neighbors, and random forests, assesses the legitimacy of a given URL. The extension communicates with the server, providing real-time notifications to users when visiting potential phishing sites. Findings: Experimental results demonstrate the effectiveness of the ensemble model with an accuracy of 90.68%, marking a significant improvement over rule-based alternatives. Unique contribution to theory, policy and practice: Future work includes refining models, incorporating user feedback, and expanding the application to diverse platforms and contexts.

Keywords: Phishing Defense; Machine Learning Classification; Web Server Architecture; Ensemble Model (search for similar items in EconPapers)
Date: 2024
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