REAL TIME AIML PHISHING DETECTION AND PREVENTION SYSTEM

Phishing attacks represent one of the most persistent and dangerous cybersecurity threats in the digital era, targeting individuals and organizations by impersonating legitimate websites to steal confidential information such as login credentials, banking details, and personal data. Traditional phishing detection methods primarily rely on blacklist databases, heuristic rules, and manual monitoring systems. However, these approaches often fail to detect newly generated phishing websites, also known as zero-day attacks, due to their dynamic and adaptive nature. To overcome these limitations, this research proposes a Real Time Artificial Intelligence and Machine Learning (AI/ML) based Phishing Detection and Prevention System capable of identifying malicious websites through automated feature analysis and predictive modeling. The system collects multiple features from website URLs, domain characteristics, page content, and security indicators such as SSL certificates. These features are then processed using machine learning algorithms to classify websites as legitimate or phishing in real time. The proposed system integrates data preprocessing, feature extraction, model training, and prediction modules to ensure efficient detection and fast response. By leveraging supervised learning techniques, the system continuously improves its accuracy by learning from large datasets of legitimate and phishing URLs. Furthermore, the real-time detection framework allows early identification of malicious websites before users interact with them, thereby reducing the risk of credential theft and financial loss. Experimental evaluation demonstrates that the proposed AI/ML-based approach significantly improves detection accuracy, reduces false positives, and provides faster identification compared to traditional blacklist approaches. This system contributes to strengthening cybersecurity infrastructure by offering an intelligent, scalable, and adaptive solution for phishing detection and prevention in modern web environments.