用强化学习训练无人战车自主应对网络攻击，实现在真实车辆上的防御能力。
Exploring reinforcement learning for incident response in autonomous military vehicles
- 在简单仿真环境训练强化学习代理，实现自主防御。
- 该代理在真实无人地面车辆上成功部署并有效响应攻击。
- 证明了模拟训练可直接赋能真实系统，适合军事自动化研究者。
无需人工干预即可执行复杂任务的无人车辆正快速发展，有望彻底改变军事作战方式。在物理与逻辑双重对抗环境下，需评估并管理其安全风险。研究指出，自主网络安全防御是推动此类车辆军事应用的关键能力之一。本文探索利用强化学习训练智能体，在军事行动背景下自主应对无人车辆的网络攻击。首先构建简易仿真环境，快速验证概念性代理；随后将其迁移至更真实的仿真平台，最终部署于实际无人地面车辆以提升真实性。本工作关键贡献在于证明：即使在简单仿真环境中训练，强化学习仍可生成适用于真实无人地面车辆的自主网络防御智能体。
原文摘要 · Abstract (English)
Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operations can be conducted. To leverage the potential of this new technology in a physically and logically contested environment, security risks are to be assessed and managed accordingly. Research on this topic points to autonomous cyber defence as one of the capabilities that may be needed to accelerate the adoption of these vehicles for military purposes. Here, we pursue this line of investigation by exploring reinforcement learning to train an agent that can autonomously respond to cyber attacks on unmanned vehicles in the context of a military operation. We first developed a simple simulation environment to quickly prototype and test some proof-of-concept agents for an initial evaluation. This agent was then applied to a more realistic simulation environment and finally deployed on an actual unmanned ground vehicle for even more realism. A key contribution of our work is demonstrating that reinforcement learning is a viable approach to train an agent that can be used for autonomous cyber defence on a real unmanned ground vehicle, even when trained in a simple simulation environment.
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