Ai-Powered Phishing Detection And Prevention

Abstract

Phishing attacks, which involve deceitful attempts to acquire sensitive information by impersonating a trustworthy entity, have become increasingly sophisticated and widespread. Traditional phishing detection methods typically rely on heuristic or signature-based techniques, which may struggle to adapt to the evolving tactics employed by attackers. This paper examines the role of artificial intelligence (AI) in enhancing phishing detection systems. AI-driven approaches utilise machine learning algorithms, natural language processing, and pattern recognition to identify and mitigate phishing threats with improved accuracy and efficiency. By analysing large datasets, our systems uncover subtle patterns and anomalies indicative of phishing attempts that conventional methods might miss. We also discuss various AI methodologies in phishing detection, including supervised and unsupervised learning techniques, ensemble methods, and deep learning models. Furthermore, we evaluate the effectiveness of AI-driven systems in real-world scenarios and their ability to adapt to new phishing strategies. Our paper concludes with a discussion of current challenges and future research directions in this field, highlighting the necessity for ongoing advancements to tackle the dynamic nature of phishing threats.