Reinforcement Learning for Adaptive Cyber Defense Training Autonomous Systems for Dynamic Threat Response and Strategy Optimization
Mohammad Alauthman, Amjad Yousef Aldweesh, Ahmad Al–Qerem, Saad Alateef, Mouhammd Sharari Alkasassbeh · Advances in computational intelligence and robotics book series · 2025
Reinforcement Learning (RL) offers a process for creating cyber defense methods that adjust to changing threats. Through training an RL agent, defenders discover ways to block intrusions, isolate systems, and reduce false positives. This chapter covers RL concepts, integration with AI security architectures, and procedures for threat response. It addresses attacker strategies, covers multiple optimization goals, and reviews data methods, reward choices, and deployment steps. Examples in healthcare, finance, and industrial networks illustrate outcomes. Governance, regulatory demands, and oversight are included, supporting RL-based cyber defense. These discussions show how RL-based defenses manage sequences and adapt to new threats. The chapter also addresses evaluation and implementation.