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

TL;DR

This paper reviews the use of Multi-Agent Reinforcement Learning in cybersecurity, highlighting its potential for adaptive defense, current applications, challenges, and future research directions in automated cyber defense systems.

Contribution

It provides a comprehensive survey of MARL applications in cyber defense, emphasizing the integration with Autonomous Cyber-defense Agents and Cyber Gyms, and discusses future challenges and opportunities.

Findings

MARL enhances intrusion detection and lateral movement containment.

Cyber Gyms are effective for training and validating MARL-based agents.

MARL faces challenges like scalability and adversarial robustness.

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.