A network threat deduction system and method based on a Transformer and a graph attention network model

The network threat simulation system based on Transformer and graph attention network models solves the destructive and periodic problems of network security testing, enables rapid and accurate threat simulation and protection optimization, and improves the efficiency and reliability of network security assessment.

CN119449452BActive Publication Date: 2026-01-23CHINESE PEOPLES LIBERATION ARMY 92493 UNIT INFORMATION TECH CENT
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Patent Information

Application Number
CN202411691503.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-25
Publication Date
2026-01-23
Estimated Expiration
2044-11-25

AI Technical Summary

Technical Problem

Existing network security testing and evaluation tools may cause irreversible damage to the network under test, and the testing cycle is uncertain, affecting the operation of business networks. There is a lack of effective auxiliary testing methods to simultaneously address destructive and periodic issues.

Method used

A network threat inference system based on Transformer and graph attention network models is adopted, including a threat intelligence extraction module, a network threat entity construction module, a network threat inference module, and an inference result application module. It constructs a knowledge graph through graph attention network, combines a decision transformer to predict attack paths and behaviors, outputs potential attack results, and verifies and optimizes protection strategies in a virtual environment.

Benefits of technology

It enables rapid and accurate simulation of network threats, reduces the impact on the services of the network under test, improves the cost-effectiveness of network assessment, and enhances the timeliness and effectiveness of protection strategies.

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Abstract

The application discloses a network threat deduction system and method based on a Transformer and a graph attention network model, wherein the system mainly comprises four modules of threat intelligence extraction, network threat entity construction, network threat deduction and deduction result application. The application utilizes a graph attention network (GAT) model to preprocess and mine network security threat intelligence data, constructs a complete network threat intelligence knowledge graph based on data correlation, and determines a vulnerability utilization weight in the graph. The application models a reasoning path sequence based on a Transformer model, predictively generates a next reasoning sequence, and realizes fast and efficient network threat intelligence reasoning. This method not only reduces the complexity of attack deduction calculation and improves deduction efficiency, but also can perform security risk investigation and early warning for a large network environment.
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