Procedia Computer Science Volume 251 , 2024 , Pages 727-733 Evaluating AI and ML in Network Security: A Comprehensive Literature Review Author links open overlay panel Ouissale El Gharbaoui a , Ikrame Kiyadi b , Hayat El Boukhari c Show more Add to Mendeley Share Cite https://doi.org/10.1016/j.procs.2024.11.176 Get rights and content Under a Creative Commons license Open access Abstract Machine learning (ML) and artificial intelligence (AI) have emerged as pivotal tools for enhancing network security in the 21st century, offering innovative approaches that surpass traditional methods. This paper conducts a comprehensive literature review to explore how AI and ML contribute to advancing network security. The findings underscore the critical role of these technologies in detecting and mitigating cyber threats, automating threat responses, and predicting future vulnerabilities. The research highlights how AI and ML can significantly enhance the accuracy and efficiency of security processes, thereby bolstering an organization's overall cybersecurity posture. Additionally, the study emphasizes the importance of seamlessly integrating AI and ML with existing security architectures to maximize their benefits. Despite the significant potential of these technologies, the study identifies several gaps in the literature, such as the need for more empirical research on the effectiveness of AI and ML-based security solutions and the practical challenges of implementing these technologies in real-world scenarios. Finally, this paper discusses the limitations of the current research and suggests future research directions to further explore the convergence of network security, AI, and ML. Previous article in issue Next article in issue Keywords Machine learning (ML) Artificial intelligence (AI) network security cyberthreats Recommended articles © 2024 The Author(s). Published by Elsevier B.V.