International Journal of Science, Engineering and TechnologyAuthors: Rohit K. Basnet
Abstract: The increasing sophistication of phishing attacks has made them one of the most persistent threats in cybersecurity. Traditional email filtering systems, which rely on static rule-based approaches, struggle to keep pace with the evolving nature of phishing techniques such as social engineering, domain spoofing, and malicious attachments. Artificial Intelligence (AI)-based email filtering systems have emerged as an effective solution by integrating machine learning, deep learning, and natural language processing (NLP) to detect and block phishing attempts with higher accuracy. These intelligent systems analyze message patterns, linguistic cues, sender reputation, and behavioral indicators to differentiate between legitimate and malicious emails. The use of adaptive learning models enables continuous improvement as the system encounters new threats. This paper explores the mechanisms of AI-based email filtering, its role in reducing phishing success rates, implementation strategies, and associated challenges. It also discusses how AI models enhance detection accuracy while maintaining usability and trust within enterprise communication systems. The findings indicate that AI-driven filtering systems not only reduce the likelihood of phishing-induced breaches but also contribute to stronger organizational resilience. Overall, AI-based email filtering represents a significant advancement toward proactive, intelligent, and adaptive cyber defense mechanisms.
DOI: https://doi.org/10.5281/zenodo.17829005
