The invention discloses a Windows domain
penetration test system and method based on deep
reinforcement learning. The
system comprises an information collection module, a
vulnerability detection module, an agent generation module and an agent training module. A host in an
intranet domain is used as a springboard
machine, an information collection module is deployed, and all network nodes in the domain are subjected to comprehensive information collection; generating a configuration file for constructing an
intelligent agent according to the information in the domain, and constructing the deep
reinforcement learning intelligent agent through the configuration file; and the
intelligent agent automatically verifies all possible potential safety hazards in the domain environment by using a
vulnerability detection module, learns in detection, and finally generates all possible safety problems in the domain. According to the method, the automatic penetration testing technology is utilized, full-automatic penetration testing and
risk assessment of the domain environment are achieved, deep
reinforcement learning is combined, various safety problems in the Windows domain are found more accurately, resources are allocated efficiently, high-risk path areas are defended in a centralized mode, and therefore the overall network safety is enhanced.