[2410.09134] Multi-Agent Actor-Critics in Autonomous Cyber Defense

Computer Science > Cryptography and Security arXiv:2410.09134 (cs) [Submitted on 11 Oct 2024 (v1), last revised 29 Mar 2026 (this version, v2)] Title:Multi-Agent Actor-Critics in Autonomous Cyber Defense Authors:Mingjun Wang, Remington Dechene View a PDF of the paper titled Multi-Agent Actor-Critics in Autonomous Cyber Defense, by Mingjun Wang and 1 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Cryptography and Se...