An industrial internet vulnerability library establishment method

By combining rule extraction, remote supervised deep learning, and graph convolutional networks to optimize confidence, as well as deep reinforcement learning and Merkle root hash chain verification, a high-quality and traceable industrial internet vulnerability database was constructed. This solved the problems of data extraction accuracy and version traceability in vulnerability database construction, and improved human-machine collaboration efficiency and deep security applications.

CN122021854BActive Publication Date: 2026-06-26北京中关村实验室
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
北京中关村实验室
Filing Date
2026-04-14
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies in industrial internet vulnerability databases suffer from problems such as insufficient accuracy in vulnerability data extraction, incomplete knowledge representation, lack of version traceability, low efficiency in human-machine collaboration, and poor adaptability to heterogeneous multi-source data. These issues result in low quality vulnerability database construction, weak traceability, insufficient continuous evolution capabilities, and difficulty in supporting in-depth security applications.

Method used

By combining rule extraction with remote supervised deep learning models, using graph convolutional networks to optimize confidence, and dynamically adjusting sampling strategies through deep reinforcement learning networks, combined with Merkle root hash chain verification, a high-quality, traceable industrial internet vulnerability knowledge base is constructed to achieve continuous evolution.

Benefits of technology

It improves the quality and reliability of vulnerability data extraction, ensures the traceability of the vulnerability database and the efficiency of compliance auditing, maximizes the efficiency of human-machine collaborative auditing, and supports the in-depth application of industrial internet security protection.

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Abstract

The present application belongs to the technical field of industrial internet security, and relates to an industrial internet vulnerability library establishment method, which solves the problems of low construction quality, weak traceability, insufficient continuous evolution ability and difficulty in supporting deep security application of the existing vulnerability library. The present application obtains industrial internet multi-source vulnerability data and external feedback data, obtains extraction results and initial confidence based on multi-source vulnerability data by adopting rule extraction and remote supervision deep learning joint extraction, divides the certainty knowledge base and the to-be-reviewed queue according to the double threshold after the confidence is optimized by the graph convolution network, analyzes the external feedback data of the samples in the to-be-reviewed queue as the instant reward signal, optimizes the sampling strategy and updates the model through deep reinforcement learning, extracts triples from the certainty knowledge base to build a knowledge graph and generate a version hash chain regularly, and obtains a structured vulnerability knowledge base containing a hash chain and confidence evaluation. The present application realizes high-precision construction and dynamic optimization of the vulnerability library.
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Citation Information

Patent Citations

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    CN121530613A

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