Reinforcement Learning Enhanced Cybersecurity Frameworks for Autonomous Threat Response Systems
R. Nithya · 2025
This chapter explores the integration of reinforcement learning (RL) into cybersecurity frameworks for autonomous threat response systems. As cyber threats become increasingly sophisticated, traditional security mechanisms struggle to provide timely and adaptive defense. RL offers a dynamic, data-driven approach to enhance the detection, mitigation, and adaptation of security measures in real-time. Key areas covered include the design of RL-based architectures, the training of agents for various attack scenarios, and the development of adaptive incident response strategies. The chapter also emphasizes continuous evaluation and improvement of RL agents to ensure optimal performance in evolving environments. Challenges such as the exploration-exploitation trade-off and the integration of feedback loops for system refinement are discussed in depth. This comprehensive analysis highlights the potential of RL to revolutionize cybersecurity operations by providing intelligent, autonomous, and adaptive threat mitigation solutions.