Real Time Phishing Website Detectionusing ML

Phishing involves fraudulent activities where attackers impersonate trustworthy websites to unlawfully obtain private information, including usernames, passwords, and financial details. Traditional detection methods, including blacklists and heuristic- based approaches, struggles identifying new, evolving phishing sites. In recent times, AI using machine learning (ML) has emerged as a powerful tool for phishing detection, offering predictive capabilities that adapt to changing attack patterns. This survey examines state- of-the-art ML techniques for phishing website detection, covering feature extraction, model types, and challenges in data handling. Through analyzing recent methodologies, this paper highlights the strengths and limitations of various ML models and proposes directions for further improving phishing detection systems.