异构智能体通过学习通信，显著提升网络攻防效率。
Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
- 用异构智能体在模拟网络中学习通信机制
- 收敛速度比其他算法快4倍，误差降低38%
- 适合研究真实网络环境下的智能攻防系统
强化学习正被探索用于应对企业网络中的网络攻击威胁。现有研究多聚焦同质多智能体系统中具备智能体间通信能力的模型。本文将研究推进至异构智能体场景，在模拟网络环境CybORG中，采用公开的先进通信算法CommFormer训练并评估智能体。结果表明，具有异构能力的CommFormer智能体在CybORG环境中表现更优：相比其他算法，其策略收敛速度最快可达4倍，标准误差改善最高达38%。该系统为人工智能在网络安全领域的研究提供了新路径，支持对真实网络环境的进一步探索。
原文摘要 · Abstract (English)
Reinforcement learning techniques are being explored as solutions to the threat of cyber attacks on enterprise networks. Recent research in the field of AI in cyber security has investigated the ability of homogeneous multi-agent reinforcement learning agents, capable of inter-agent communication, to respond to cyberattacks. This paper advances the study of learned communication in multi-agent systems by examining heterogeneous agent capabilities within a simulated network environment. To this end, we leverage CommFormer, a publicly available state-of-the-art communication algorithm, to train and evaluate agents within the Cyber Operations Research Gym (CybORG). Our results show that CommFormer agents with heterogeneous capabilities can outperform other algorithms deployed in the CybORG environment, by converging to an optimal policy up to four times faster while improving standard error by up 38%. The agents implemented in this project provide an additional avenue for exploration in the field of AI for cyber security, enabling further research involving realistic networks.
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