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.

本研究解决了无人车辆在军事行动中面临的网络攻击响应能力不足的问题。我们创新性地采用强化学习训练代理，使其能在真实的无人地面车辆上自主应对网络攻击，并证明了该方法在简单模拟环境中也能有效训练出相关能力。研究结果表明，强化学习是一种有效的自主网络防御方法，具有提升无人军事车辆作战能力的潜力。

探索强化学习在自主军事车辆事件响应中的应用