The application discloses a network
space mapping method based on weak supervision learning, comprising the following steps: S1, establishing a
public network space mapping IP address library, and identifying known IP information; using self-owned basic resource data to collect information of IP with relatively clear unit attribution; S2, identifying
IP address association information of non-known
IP address. In the application, through self-developed asset identification
algorithm, weak supervision learning
algorithm is used to extract website features, high-precision asset labels are made, and internet assets are spatially mapped. The main contents of mapping include IP street-level geographic location, industry classification, IP port
service information,
certificate information, website feature information, etc. In a manner of combining
spatial mapping map and vector
topographic map, data is presented. The network
space mapping map is the infrastructure for realizing digital production and life and digital governance in the digital era, and is of great significance for providing
network security event monitoring and analysis,
emergency response and
attack tracing.