Dynamic behavior authentication security system and method for industrial Internet of Things equipment

By using a dynamic behavior authentication system, a Behavior Consistency Index (BCI) is generated by aggregating multi-channel signal streams and unweighted norms. Combined with an active challenge to calculate the Coherence Consistency Index (ACCI), the problem of authentication being vulnerable to attacks and misjudgments in existing technologies is solved, thus achieving accurate identity authentication and security for industrial IoT devices.

CN121967035APending Publication Date: 2026-05-01SHENZHEN HENGKONG TECH CO LTD
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Patent Information

Application Number
CN202610157506.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-04
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing industrial IoT device authentication methods rely on static keys and single network parameters, which are vulnerable to attacks, have a high false positive rate, and lack security. In particular, they can easily lead to the tampering or interruption of the production process in industrial settings.

Method used

A dynamic behavior authentication system is adopted, which acquires multi-channel signal streams through a data acquisition module, performs baseline denoising and distributed processing, generates the Behavioral Consistency Index (BCI) by combining unweighted norm aggregation, and calculates the Coherence Consistency Index (ACCI) through active challenge to achieve dual evaluation and security measures.

Benefits of technology

It enables precise identification of industrial IoT devices, reduces the detection capability of spoofed devices, improves the robustness and accuracy of authentication, avoids misjudgment and production interruption, and ensures the safety and continuity of industrial production.

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Abstract

The invention discloses a dynamic behavior authentication security system and method for industrial Internet of Things equipment, and relates to the technical field of authentication security. During operation of the system, equipment dynamic parameters including electromagnetic radiation spectrum, power supply ripple, micro-current jitter, micro-vibration entropy, thermal transient recovery, clock phase noise and network delay jitter are acquired; the method comprises the steps of forming an original multi-channel signal flow, performing preprocessing, constructing a multi-working-condition reference fingerprint database, calculating deviation of each parameter based on a distribution distance, aggregating the deviation into physical layer deviation and network layer deviation through an unweighted norm mode, generating a behavior consistency index BCI based on the deviation of the physical parameter and the network parameter, performing first evaluation and grading judgment, and performing second evaluation and grading judgment. According to the method, perturbation challenges are injected into gray area equipment, multi-dimensional responses under perturbation are collected, an active challenge coherence consistency index ACCI is calculated, and equipment authority adjustment, network segment isolation, degraded operation or safety linkage measures are executed based on double evaluation results of the BCI and the ACCI.
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