Secure Browse: AI-Powered Phishing Defense for Browsers
DOI:
https://doi.org/10.47941/ijce.1921Keywords:
Phishing Defense, Machine Learning Classification, Web Server Architecture, Ensemble ModelAbstract
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.
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References
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Copyright (c) 2024 Santosh Kumar Kande
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
