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

Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning