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

Christoph R Landolt; Christoph Würsch; Roland Meier; Alain Mermoud; Julian Jang-Jaccard

TL;DR

A survey of multi-agent reinforcement learning for automated cyber defense, covering intruder detection, lateral movement containment, and the role of Cyber Gyms in training and validating autonomous defense agents.

Abstract

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.

BibTeX

@inproceedings{landolt2025marl,
  title         = {Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications},
  author        = {Landolt, Christoph R. and W\"ursch, Christoph and Meier, Roland and Mermoud, Alain and Jang-Jaccard, Julian},
  booktitle     = {International Conference on Military Communication and Information Systems (ICMCIS)},
  year          = {2025},
  eprint        = {2505.19837},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CR}
}