AI vs. Cybercrime: Revolutionising Phishing Detection Through Machine Learning

Phishing has evolved into a primary cyber security attack vector that leverages human and technical vulnerabilities to obtain sensitive information, financial credentials, and personal data. Sophisticated attacks, especially those using dynamically generated websites, misleading emails, and social engineering tactics, often elude detection by traditional phishing detection systems. Artificial Intelligence (AI) and Machine Learning (ML) are promising solutions which can achieve automated analysis, recognition of behavioural patterns and real-time detection of malicious activities. This study explores the potential of machine learning to revolutionise phishing detection and improve cybersecurity systems. It looks at the use of supervised and unsupervised learning algorithms, natural language processing, and feature extraction techniques to identify genuine digital communications from fraudulent attempts. The study also tackles problems concerning data quality, evolving attack patterns, false positives, and adversarial attacks. The research highlights the role of AI-driven strategies in improving detection precision, response effectiveness, and cyber resilience by evaluating the potential of intelligent detection systems, thereby supporting the creation of adaptive, proactive, and dependable cybersecurity mechanisms.