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@article{207320,
        author = {Monish S and Vinay Gowda A and Naveen J},
        title = {AI-Powered Phishing Website Detection Using Machine Learning and Chrome Extension Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {402-408},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207320},
        abstract = {Phishing attacks continue to rank among the most damaging forms of cybercrime today, with millions of individuals losing sensitive credentials and financial assets through fraudulent websites that convincingly replicate trusted platforms. Conven-tional defences such as browser warnings and URL blacklists remain fundamentally reactive, leaving users exposed during the early hours of a new attack campaign. This paper presents a fully functional, end-to-end phishing detection system built around machine learning. Three classifiers were trained on a real-world dataset of 11,055 labelled websites, with 31 features drawn from URL structure, domain properties, and page content. Among these, Random Forest delivered the strongest results: 94.75% accuracy, Precision 94.81%, Recall 94.75%, F1-Score 94.74%, and AUC 0.99. The trained model was packaged as a Flask REST API and paired with a Google Chrome extension that surfaces a colour-coded safety alert within 200 ms. The entire pipeline runs on a standard laptop without a GPU or cloud dependency, making it practical for everyday use.},
        keywords = {Phishing Detection, Machine Learning, Random Forest, Decision Tree, Logistic Regression, Chrome Extension, Flask REST API, URL Feature Extraction, Cybersecurity, Real-Time Detection, Ensemble Learning, UCI Dataset},
        month = {August},
        }