Real-Time Phishing Website Detection using Machine Learning and Updating Phishing Probability with User Feedback

Abstract

Phishing attacks remain a significant threat to internet users worldwide. Cybercriminals often send out phishing links through various channels such as emails, social media platforms, or text messages, to trick users into disclosing their sensitive infor-mation such as passwords, usernames, or credit card details. This stolen information is then used to perpetrate various types of fraud or sold on the dark web for profit. To combat this problem, various machine learning-based solutions have been developed for detect-ing phishing websites. However, these solutions vary in their effec-tiveness, with some focusing on URL-based algorithms while oth-ers focus on website content. This paper proposes a machine learn-ing-based approach to real-time phishing website detection, with a focus on the website's URL, domain page, and content. The pro-posed framework will be implemented as a browser plug-in, which can identify phishing risks as users visit websites. The framework integrates several techniques, including blacklist interception, whitelist filtering, and machine learning prediction, to improve ac-curacy, reduce false alarm rates, and minimize computation times. The proposed approach also incorporates user feedback to update the phishing probability over time, thereby increasing the accuracy of detecting phishing websites. This feedback loop involves users reporting suspected phishing websites to the system, which then updates the phishing probability calculation with new information to improve its accuracy. The significance of this research lies in its ability to provide real-time phishing detection capabilities, which can help protect internet users from falling victim to phishing at-tacks. Furthermore, the use of machine learning-based algorithms and user feedback ensures that the system is continuously updated to remain effective against new and emerging threats.