AI-Driven Phishing Website Detection with Blockchain-Powered Secure Threat Logging

– Using counterfeit websites, phishing assaults have grown a major cybersecurity menace since they use trust of consumers to grab valuable data. Blacklists, which cannot identify new or developing phishing sites in real time, are frequently the foundation of conventional phishing detection approaches. Although machine learning-based techniques have shown potential, obstacles stay in guaranteeing accurate, live detection and secure record keeping of recognized hazards. This project introduces an Advanced Phishing Website Detection System that uses blockchain technology and machine learning to provide a secure and fast answer to these issues. From URLs, the system first extracts important characteristics, preprocesses them, and then uses machine learning models Random Forest, Multi-Layer Perceptron (MLP), XGBoost, and Support Vector Machine (SVM) to classify websites as phishing or genuine. Using a Flask-based web application, the trained models have high accuracy since MLP is used for real-time detection. The MLP model parses and examines the characteristics of a URL provided by a user; if it is found to be phishing, the URL is stored in a blockchain ledger to assure tamper-proof logging of phishing sites. If not the case, the user has website access. Users can also see every identified phishing site on a particular page, therefore raising knowledge and proactive protection. The incorporation of blockchain technology improves transparency, security, and trust in the detection system and offers a powerful, live, and scalable strategy for combating phishing threats.