Deep learning based network threat detection and response system

By constructing an event correlation matrix and generating dynamic response instructions through a deep learning-based network threat detection system, the problems of cross-regional attack chain tracing and inaccurate threat assessment are solved, achieving the synergy, accuracy and real-time nature of network security.

CN121690840BActive Publication Date: 2026-07-24SHENZHEN ZHIHECHUANGWEI INFORMATION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHENZHEN ZHIHECHUANGWEI INFORMATION TECH CO LTD
Filing Date
2025-12-30
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing network threat detection systems lack cross-regional collaboration mechanisms, making it difficult to trace attack chains, resulting in inaccurate threat assessments, mismatches between response strategies and the actual threat landscape, and a lack of real-time dynamic adjustment capabilities.

Method used

A deep learning-based network threat detection and response system collects data from multiple regions, constructs an event correlation matrix, calculates the three-dimensional correlation of time, space, and behavior, and generates blocking, isolation, and rate limiting commands to achieve accurate identification and dynamic response to cross-regional attack chains.

Benefits of technology

It achieves collaborative, accurate, and real-time network security operations, and can automatically generate access control policies based on attack chain analysis results, improving the ability to trace cross-regional attack chains and the intelligent automation of response.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the technical field of network threat detection, and discloses a network threat detection and response system based on deep learning. The system comprises a collection module, a statistical module and an instruction generation module. The collection module is used for collecting standardized data sets from a plurality of preset security areas. The statistical module is used for statistically calculating attack equivalent cumulative values, defense strength indexes and asset attack exposure degrees based on the standardized data sets. The instruction generation module is used for calculating a threat comprehensive score according to the attack equivalent cumulative values, the asset attack exposure degrees and the defense strength indexes, and generating blocking instructions, isolation instructions and flow limiting instructions according to the threat comprehensive score. The application can automatically generate an access control strategy according to an attack chain analysis result and execute the access control strategy, thereby forming a complete closed loop from threat detection to strategy optimization, and improving the synergy, accuracy and real-time performance of network security operation.
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