The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber defense, leveraging the collaborative interactions among multiple agents to detect, mitigate, and respond to cyber threats. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios. The results indicate that MADRL can significantly enhance the capability of autonomous cyber defense systems, paving the way for more intelligent cybersecurity strategies. This study contributes to the growing body of knowledge on leveraging artificial intelligence for cybersecurity and sheds light for future research and development in autonomous cyber operations.

本研究解决了在快速演变的网络威胁环境中缺乏自主适应防御机制的问题。论文提出了一种基于多智能体深度强化学习的创新方法，通过多智能体演员-评论者算法提升网络防御效率。研究结果表明，这种方法显著增强了自主网络防御系统的能力，对未来的智能网络安全策略具有重要影响。

自主网络防御中的多智能体演员-评论者算法