Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197440,
        author = {Mubeen Begum and Mahammad Abdul Thayeeb and Mohammad Mujtaba Mukaram and Md Abdul Wahid Ekram},
        title = {Real-Time Phishing Detection Using Random Forest and XGBoost: A Chrome Extension for Safer Browsing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7485-7499},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197440},
        abstract = {In the modern digital era, the proliferation of online services has led to a corresponding increase in web spoofing and phishing attacks, where malicious actors create deceptive copies of legitimate websites to steal sensitive user information. This paper presents Phish Catcher, a client-side defense mechanism designed to protect users from such attacks. As a proof of concept, we developed a Google Chrome extension that implements a machine learning-based classifier to determine whether a login web page is legitimate or spoofed. Our approach utilizes the Random Forest algorithm, which takes four different types of web features as input to make its classification. To evaluate the effectiveness of Phish Catcher, we conducted extensive experiments on real-world web applications. The results demonstrate remarkable performance, achieving an accuracy of 98.5},
        keywords = {Phishing Detection, Web Spoofing, Machine Learning, Random Forest, Client-side Security, Google Chrome Extension, Cyber Security.},
        month = {April},
        }