Multi-Agent Actor-Critics in Autonomous Cyber Defense
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 11 Oct 2024.
Builds on
17 of 26 references.
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5 more
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
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- 2025An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
- 2025Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security
- 2024Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security