用多智能体强化学习实现自适应网络安全防御，提升对抗复杂攻击的能力。
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
- 采用多智能体强化学习构建分布式协同防御机制
- 在入侵检测和横向移动遏制中展现有效防御能力
- 适合研究自动化攻防系统与智能安全代理的开发者
多智能体强化学习（MARL）在应对现代网络安全挑战方面展现出巨大潜力，能够实现去中心化、自适应且协作的防御策略，并提供自动化机制以应对动态、协同且复杂的威胁。本文综述了MARL在自动化网络防御（ACD）中的应用现状，重点关注入侵检测与横向移动遏制。同时，探讨了自主智能网络防御代理（AICA）及网络攻防训练环境（Cyber Gyms）在训练与验证MARL代理中的作用。文章还分析了当前面临的可扩展性与对抗鲁棒性等挑战，并提出未来研究方向。研究表明，MARL通过集成于AICA，可为日益复杂的网络威胁提供自适应、可扩展和动态的解决方案，尤其在入侵检测与横向移动遏制方面具有变革性潜力，且Cyber Gyms对AICA的训练与评估至关重要。
原文摘要 · Abstract (English)
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.
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