  In this work, we present a learning-based approach to analysis cyberspace
configuration. Unlike prior methods, our approach has the ability to learn from
past experience and improve over time. In particular, as we train over a
greater number of agents as attackers, our method becomes better at rapidly
finding attack paths for previously hidden paths, especially in multiple domain
cyberspace. To achieve these results, we pose finding attack paths as a
Reinforcement Learning (RL) problem and train an agent to find multiple domain
attack paths. To enable our RL policy to find more hidden attack paths, we
ground representation introduction an multiple domain action select module in
RL. By designing a simulated cyberspace experimental environment to verify our
method. Our objective is to find more hidden attack paths, to analysis the
weakness of cyberspace configuration. The experimental results show that our
method can find more hidden multiple domain attack paths than existing
baselines methods.
