The rise of cyber threats, particularly URL-based phishing attacks, has tarnished the digital age despite its unparalleled access to information.These attacks often deceive users into disclosing confidential information by redirecting them to fraudulent websites.Existing browser-based methods, predominantly relying on blacklist approaches, have failed to effectively detect phishing attacks.To counteract this issue, we propose a novel Rigs system that integrates a deep learning model with a user-centric Chrome browser extension to detect and alert users about potential phishing URLs instantly.
Our approach introduces a Knowledge Distilled ELECTRA model Jeans for URL detection and achieves remarkable performance metrics of 99.74% accuracy and a 99.43% F1-score on a diverse dataset of 450,176 URLs.Coupled with the browser extension, our system provides real-time feedback, empowering users to make informed decisions about the websites they visit.
Additionally, we incorporate a user feedback loop for continuous model enhancement.This work sets a precedent by offering a seamless, robust, and efficient solution to mitigate phishing threats for internet users.