Comparative Analysis of AI Algorithms for Enhancing Phishing Detection in Real-Time Email Security

Abstract

This research provides a comparative analysis of widely-used AI algorithms, including decision trees, random forests, support vector machines (SVM), neural networks, and recurrent neural networks (RNN), for their ability to analyze email metadata, content, and embedded links to identify phishing attempts. The study examines the strengths and weaknesses of each algorithm in terms of accuracy, speed, and robustness in detecting phishing emails in real time. It also evaluates how well these algorithms adapt to evolving phishing tactics, such as spear-phishing and AI-generated phishing content. Furthermore, the paper highlights the role of NLP techniques in analyzing email language and tone, detecting suspicious patterns, and identifying deceptive or manipulative language typically used in phishing attempts. By comparing multiple AI approaches, the study reveals how combinations of these methods—such as ensemble learning or hybrid models—can improve phishing detection rates and reduce false positives, enhancing user experience and security performance. Through practical case studies and experiments, this research demonstrates the impact of AI-enhanced phishing detection algorithms on organizational security. It concludes by offering insights into best practices for integrating AI-driven phishing detection systems into real-time email security frameworks, helping organizations better protect sensitive data and reduce the risks associated with phishing attacks.