Abstract: Phishing attacks have emerged as one of the most prevalent cybersecurity threats, targeting unsuspecting users by imitating legitimate websites to steal sensitive information. This project aims to develop an advanced phishing detection and prevention system using machine learning and browser extension-based security awareness. The system is designed to simulate phishing websites, analyze user interactions, and extract key dataset features to improve phishing detection mechanisms.
The proposed solution consists of three core components:
1. Phishing Website Simulation – A controlled environment where phishing websites are created to mimic real-world attack patterns. User interactions are analyzed, and dataset features such as URL structure, SSL certificate status, and JavaScript behavior are extracted to enhance detection accuracy.
2. Machine Learning-Based Detection – The system trains various machine learning models (Random Forest, Support Vector Machine (SVM), and Neural Networks) using datasets of phishing and legitimate websites. Key extracted features like URL length, domain age, presence of HTTPS, and script execution patterns help in real-time classification to differentiate between phishing and authentic sites.
3. Browser Extension for Prevention – A real-time browser extension that integrates with the machine learning model to scan webpages before loading. It warns users via pop-up notifications when a phishing attempt is detected and blocks access to malicious websites. The extension also logs phishing attempts, displaying IP addresses, geolocation, and additional metadata for further research and reporting.
The system architecture follows a multi-layered approach, leveraging client-side security mechanisms, cloud-based threat intelligence, and AI-driven classification for effective phishing detection. The methodology ensures real-time protection for users while also generating datasets for continuous model training and enhancement.
This project contributes to enhancing cybersecurity awareness by educating users about phishing tactics and equipping them with proactive security measures. Additionally, real-time logging of phishing attempts provides cybersecurity researchers and organizations with valuable data to refine detection strategies and mitigate threats.
Through extensive testing and validation, the proposed phishing detection and prevention framework achieves high accuracy in identifying phishing attempts while maintaining low false positive rates. Future scope includes expanding detection capabilities to mobile browsers and integrating blockchain-based threat validation for added security.
This project ultimately aims to empower users with real-time phishing protection, improve cybersecurity resilience, and enhance global efforts in combating phishing-related cyber threats.
Keywords: Phishing Attack, Cyber Security, Machine Learning, Artificial Intelligence, Browser Extension, Website Detection, Cybercrime Prevention
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DOI:
10.17148/IJARCCE.2025.145119
