The escalating sophistication, frequency, and unpredictability of cyberattacks necessitate defense mechanisms that can dynamically adapt to evolving threat landscapes. Traditional static security solutions, while effective against known attack vectors, often fail to counter zero-day exploits, advanced persistent threats (APTs), and adversaries employing adaptive tactics. Reinforcement Learning (RL) offers a promising paradigm for adaptive cyber defense, enabling systems to learn optimal defense strategies through continuous interaction with dynamic environments. This paper investigates the role of RL in developing intelligent, self-optimizing security frameworks capable of real-time decision-making in intrusion detection, network traffic analysis, malware mitigation, and automated incident response. By modeling the cyber defense problem as a sequential decision-making process, RL agents leverage reward functions to balance trade-offs between proactive prevention, timely detection, and efficient recovery from cyber incidents. Techniques such as Deep Q-Networks (DQN), Policy Gradient Methods, Actor–Critic architectures, and Multi-Agent Reinforcement Learning (MARL) are examined for their applicability to diverse cybersecurity scenarios. The proposed RL-based adaptive defense framework incorporates situational awareness by integrating multiple data sourcessuch as network telemetry, system logs, and threat intelligence feedsallowing for context-aware threat prioritization and response orchestration. Simulation experiments using benchmark datasets and emulated attack scenarios demonstrate that RL-driven defense systems can outperform conventional static rule-based models by reducing false positives, minimizing response latency, and dynamically reallocating resources to protect critical assets. Moreover, the study addresses challenges such as reward shaping, convergence stability, exploration–exploitation balance, and adversarial manipulation of RL policies. Strategies for integrating explainable RL to enhance transparency, compliance, and analyst trust are also discussed. Practical deployment considerations, including scalability, interoperability with existing Security Information and Event Management (SIEM) systems, and alignment with AI governance standards, are explored. The findings underscore the transformative potential of RL in achieving adaptive, resilient, and proactive cyber defense postures, contributing to the next generation of intelligent security systems capable of anticipating and countering sophisticated cyber threats in real time.
Cadet et al. (Fri,) studied this question.