多智能体强化学习让多个防御代理协同应对网络攻击。
Multi-Agent Actor-Critics in Autonomous Cyber Defense
- 采用多智能体演员-评论家算法实现分布式自主防御
- 在模拟攻击中各代理能快速学习并独立响应威胁
- 适合研究智能安全系统与自主防御的学者
随着网络威胁环境快速演变，实现自主且自适应的防御机制已成为当务之急。多智能体深度强化学习（MADRL）为提升自主网络操作的效能与韧性提供了有前景的解决方案。本文探讨了多智能体演员-评论家算法在网络安全防御中的应用，该算法通过多个智能体间的协作互动，实现对网络威胁的检测、缓解与响应。实验表明，在模拟网络攻击场景中，各智能体能够利用MADRL快速学习并自主应对威胁。结果表明，MADRL显著提升了自主网络安全系统的防护能力，为更智能的网络安全策略铺平了道路。本研究推动了人工智能在网络安全领域的应用，为未来自主网络操作的研究与开发提供了方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。