mlsanityData to 14 Oct 2026

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications

arXiv 2505.19837 ↗First posted 26 May 2025cs.MA cs.GT cs.LGcited by 1

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Abstract (arXiv)

Multi-Agent Reinforcement Learning (MARL) has shown great potential as an adaptive solution for addressing modern cybersecurity challenges. MARL enables decentralized, adaptive, and collaborative defense strategies and provides an automated mechanism to combat dynamic, coordinated, and sophisticated threats. This survey investigates the current state of research in MARL applications for automated cyber defense (ACD), focusing on intruder detection and lateral movement containment. Additionally, it examines the role of Autonomous Intelligent Cyber-defense Agents (AICA) and Cyber Gyms in training and validating MARL agents. Finally, the paper outlines existing challenges, such as scalability and adversarial robustness, and proposes future research directions. This also discusses how MARL integrates in AICA to provide adaptive, scalable, and dynamic solutions to counter the increasingly sophisticated landscape of cyber threats. It highlights the transformative potential of MARL in areas like intrusion detection and lateral movement containment, and underscores the value of Cyber Gyms for training and validation of AICA.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 70 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

27 of 70 references.

References · 27

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15 more
  1. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 33
  2. 2022A Theory of Abstraction in Reinforcement Learningcited by 27
  3. 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 23
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypescited by 22

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