Risk monitoring method based on intelligent association and global situation of multi-source data

By integrating multi-source heterogeneous data and utilizing graph neural networks and causal reasoning technology to generate a network security situation map, the shortcomings of existing network security systems in multi-source data processing and global situation awareness are addressed, achieving efficient threat detection and rapid response.

CN120675823AActive Publication Date: 2025-09-19INFORMATION & COMM CO OF STATE GRID SHAANXI ELECTRIC POWER CO LTD
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Patent Information

Application Number
CN202511180622.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-22
Publication Date
2025-09-19
Estimated Expiration
2045-08-22

AI Technical Summary

Technical Problem

Existing network security systems lack comprehensive analysis capabilities when faced with multi-source heterogeneous data, making it difficult to cope with complex attack chains. They also have deficiencies in real-time and global situational awareness, resulting in low threat detection efficiency and difficult operation and maintenance.

Method used

By introducing causal reasoning and improved graph neural networks (GNN), multi-source heterogeneous data is integrated, attack behavior correlation maps are generated, anomaly detection is performed, explicit and potential attack links are obtained, and risk assessment reports are generated based on the network security situation dynamic map to trigger security policies.

Benefits of technology

It significantly improves the restoration capability and prediction accuracy of complex attack scenarios, reduces false alarm and missed alarm rates, realizes real-time threat detection and situation updates, reduces the operation and maintenance burden, and improves the global network security situation awareness capability.

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Abstract

The invention belongs to the technical field of network security, and particularly relates to an intelligent association and global situation risk monitoring method based on multi-source data, which comprises the following steps: acquiring a multi-source heterogeneous data set; the multi-source heterogeneous data set is preprocessed, and preprocessed multi-source data is obtained; obtaining an attack behavior association graph according to the multi-source data, and performing anomaly detection on the association graph by using a graph neural network to obtain an explicit attack link and a potential attack link; obtaining a network security situation dynamic graph based on the explicit attack link and the potential attack link; and generating a risk assessment report according to the network security situation dynamic graph, triggering a corresponding security policy, and performing risk monitoring according to the security policy. According to the method, the accuracy and response speed of threat detection are remarkably improved, the global network security situation awareness capability is enhanced, and an intelligent solution is provided for security protection in a complex network environment.
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