Dynamic network access control method and system based on zero-trust architecture

By using dynamic network access control based on a zero-trust architecture, combined with triple binding of biometrics, device fingerprints, and environmental status, and leveraging machine learning and AI-driven real-time risk quantification and access control, the static lag problem of traditional network access control is solved, enabling real-time response and adaptive defense against dynamic threats.

CN121923907APending Publication Date: 2026-04-24TAISHAN UNIV
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Patent Information

Application Number
CN202610100922.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-26
Publication Date
2026-04-24

AI Technical Summary

Technical Problem

Traditional network access control methods rely on static rule bases, which are difficult to respond to dynamic security threats in real time. This makes it difficult to defend against internal threats and external attacks, and the lagging access control leads to data leakage.

Method used

A dynamic network access control method based on zero-trust architecture is adopted. Through triple binding of biometrics, device fingerprints and environmental status, combined with real-time risk quantification by machine learning and AI-driven policy generation, real-time upgrade and downgrade of permissions and fine-grained control are achieved. Security and transparency are ensured by using agentless API gateways and blockchain to store logs.

Benefits of technology

It enables real-time response to dynamic threats and adaptive adjustment of permissions, forming a continuous adaptive defense system. This avoids the static lag problem of traditional methods and improves the real-time defense capability and strategy optimization efficiency of network security.

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Abstract

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.
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