Phishing Attack Stimulation and Prevention Mechanism

Abstract

Phishing attacks have become one of the most prevalent cyber security threats, exploiting social engineering techniques to deceive users into revealing sensitive information such as passwords, banking details, and personal data. Cybercriminals continuously refine their tactics, making traditional security measures like blacklists and rule-based filters less effective. To address this challenge, we propose a machine learning (ML)–based browser extension designed to detect and prevent phishing attempts in real-time. Initially developed for Google Chrome but adaptable to other browsers, the extension operates in persistent “pin mode”, ensuring continuous monitoring of URLs entered by users. By extracting key features such as domain attributes, SSL certificate details, URL structure, and lexical patterns, the extension employs a trained ML model to accurately classify web pages as legitimate or potentially malicious. When a phishing attempt is detected, the extension immediately alerts the user with warnings, preventing them from interacting with fraudulent sites. This solution outperforms traditional blacklist-based approaches by leveraging AI to detect emerging threats, making it more robust against evolving phishing techniques. Additionally, the extension is designed to be lightweight and user-friendly, ensuring it does not compromise browser performance while providing seamless, proactive protection against online scams. Future enhancements could incorporate behavioral analysis, adaptive learning techniques, and integration with external threat intelligence feeds to further improve detection accuracy and resilience against new phishing strategies.