Beyond Reinforcement Learning for network security: A comprehensive survey and tutorial

Maintaining strong security is a complex yet vital challenge in the rapidly evolving landscape of modern digital networks. The risks and consequences of security breaches make neglecting network protection unacceptable. Fortunately, ongoing advances in computer science have equipped researchers with powerful tools to reinforce network defenses. Among these, Reinforcement Learning (RL), a branch of machine learning, has gained significant attention for its versatility and effectiveness in strengthening security mechanisms. This paper presents a comprehensive survey and tutorial on the role of RL in network security. It provides background information, a step-by-step tutorial for training RL models, and systematically categorizes research efforts based on the targeted cyber threats. Leveraging recent advances and real-world applications, this survey elucidates how RL enables the development of adaptive and intelligent systems that autonomously learn and respond to evolving threats. Through in-depth analysis, we provide a comprehensive view of the current landscape and the future potential of RL in safeguarding digital assets. The main contributions of this survey are: (1) a systematic and up-to-date review of RL approaches for network security; (2) a unified taxonomy for classifying RL-based solutions; (3) a comparison of the latest advances from 2019 to 2024 across mainstream and emerging research areas; (4) identification of open challenges and future research directions; and (5) a comparative analysis of state-of-the-art models, offering practical insights for both researchers and practitioners. Furthermore, this survey emphasizes the practical translation of RL advances into real-world deployments. By focusing on hands-on implementation guidelines and comparative analyses of deployment scenarios, it bridges the gap between academic research and operational practice. The comprehensive evaluation of RL-based models across different network environments provides actionable insights for practitioners seeking adaptive and scalable security solutions in dynamic and heterogeneous settings.