  In general, multiple domain cyberspace security assessments can be
implemented by reasoning user's permissions. However, while existing methods
include some information from the physical and social domains, they do not
provide a comprehensive representation of cyberspace. Existing reasoning
methods are also based on expert-given rules, resulting in inefficiency and a
low degree of intelligence. To address this challenge, we create a Knowledge
Graph (KG) of multiple domain cyberspace in order to provide a standard
semantic description of the multiple domain cyberspace. Following that, we
proposed a user's permissions reasoning method based on reinforcement learning.
All permissions in cyberspace are represented as nodes, and an agent is trained
to find all permissions that user can have according to user's initial
permissions and cyberspace KG. We set 10 reward setting rules based on the
features of cyberspace KG in the reinforcement learning of reward information
setting, so that the agent can better locate user's all permissions and avoid
blindly finding user's permissions. The results of the experiments showed that
the proposed method can successfully reason about user's permissions and
increase the intelligence level of the user's permissions reasoning method. At
the same time, the F1 value of the proposed method is 6% greater than that of
the Translating Embedding (TransE) method.
