The field of cyber defense is moving towards the development of more adaptive and responsive strategies to counter evolving threats. Researchers are exploring the use of machine learning and game theory to model interactions between attackers and defenders, with a focus on creating more realistic and dynamic simulations. One key area of innovation is the use of multi-agent reinforcement learning to train defender agents that can generalize against a range of unknown opponents. Another important direction is the development of frameworks that can accurately represent cybersecurity scenarios and produce informative training signals for reinforcement learning agents. Notable papers include:

• Adaptive Learning for Moving Target defence: Enhancing Cybersecurity Strategies, which proposes a structure-aware policy gradient reinforcement learning algorithm to help defenders adapt to evolving threats.
• PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense, which introduces a new environment for training defenders against advanced adversaries.
• Towards Production-Worthy Simulation for Autonomous Cyber Operations, which presents a framework for extending simulated environments to better represent real-world cybersecurity scenarios.