  In this work, we present a learning-based approach to analysis cyberspace
security 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
discovering hidden attack paths for previously methods, especially in
multi-domain cyberspace. To achieve these results, we pose discovering attack
paths as a Reinforcement Learning (RL) problem and train an agent to discover
multi-domain cyberspace attack paths. To enable our RL policy to discover more
hidden attack paths and shorter attack paths, we ground representation
introduction an multi-domain action select module in RL. Our objective is to
discover more hidden attack paths and shorter attack paths by our proposed
method, to analysis the weakness of cyberspace security configuration. At last,
we designed a simulated cyberspace experimental environment to verify our
proposed method, the experimental results show that our method can discover
more hidden multi-domain attack paths and shorter attack paths than existing
baseline methods.
