Reinforcement Learning for Automated Cyber Defense in Dynamic Attack Environments

Abstract

The rapid evolution of cyber threats and the complexity of dynamic attack environments have rendered traditional rule-based security systems increasingly ineffective. This research explores the application of reinforcement learning (RL) for the development of intelligent, autonomous cyber defense mechanisms capable of adapting in real time to evolving attack strategies. By modeling cybersecurity as a sequential decision-making problem, RL agents learn optimal defense strategies through trial-and-error interactions within simulated environments. We present a comprehensive framework integrating deep reinforcement learning (DRL) with network intrusion detection and response systems to demonstrate the viability of adaptive, automated defense. The research includes the design of training environments using adversarial models, evaluation of agent performance under various attack scenarios, and comparative analysis against static defense systems. Results reveal significant improvements in detection accuracy, response efficiency, and resilience against novel threats, underscoring the potential of RL as a core enabler of future cyber defense automation.