Recent advancements in deep learning techniques have opened new possibilities for designing solutions for autonomous cyber defence. Teams of intelligent agents in computer network defence roles may reveal promising avenues to safeguard cyber and kinetic assets. In a simulated game environment, agents are evaluated on their ability to jointly mitigate attacker activity in host-based defence scenarios. Defender systems are evaluated against heuristic attackers with the goals of compromising network confidentiality, integrity, and availability. Value-based Independent Learning and Centralized Training Decentralized Execution (CTDE) cooperative Multi-Agent Reinforcement Learning (MARL) methods are compared revealing that both approaches outperform a simple multi-agent heuristic defender. This work demonstrates the ability of cooperative MARL to learn effective cyber defence tactics against varied threats.

深度学习技术的最新进展为自主网络防御的设计提供了新的可能性，智能代理团队在计算机网络防御角色中可能揭示了保护网络和运动资产的有希望的途径，该研究对比了基于价值的独立学习和集中训练去中心化执行的合作多代理强化学习方法，表明这两种方法都优于简单的多代理启发式防御者，这项工作展示了合作多代理强化学习在学习有效的网络防御策略对抗不同威胁方面的能力。

用多智-Agent 强化学习从零开始学习网络防御策略