A multi-source cross-domain network security big data fusion management and collaborative service method
By accessing multi-source heterogeneous data and standardizing modeling, a security behavior graph is constructed and entity context embedding is performed using graph neural networks. This solves the problems of difficulty in fusion of multi-source heterogeneous data and insufficient cross-domain collaboration, thereby improving the situational awareness and response capabilities of network security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-02-07
- Publication Date
- 2026-06-02
AI Technical Summary
Existing cybersecurity data processing methods suffer from problems such as difficulty in data fusion, low governance efficiency, large semantic differences, insufficient cross-domain collaboration, and insufficient intelligence in multi-source heterogeneity and cross-domain collaboration, and cannot meet the security challenges of high intensity, high frequency, and high complexity.
By accessing multi-source heterogeneous data and standardizing modeling, a security behavior graph is constructed and entity context embedding is performed using graph neural networks. Combined with semantic ontology constraints and graph embedding learning, attack chains are identified and causal reasoning is performed to achieve cross-domain collaborative response.
It enables unified access and deep integration of multi-source heterogeneous data, enhances network security situational awareness and response capabilities, supports rapid and unified policy formulation and coordinated execution, and improves the efficiency and security of data value utilization.
Smart Images
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