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Anonymous and Efficient Chaotic Map-Based Authentication Protocol for Industrial Internet of Things.

Dake Zeng¹, Akhtar Badshah², Shanshan Tu¹

¹College of Computer Science, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This study introduces a new security protocol for the Industrial Internet of Things (IIoT) to protect sensitive data. It uses advanced cryptography and biometrics for secure user authentication and access control in IIoT systems.

Keywords:

Industrial Internet of Things authentication biometrics key agreement privacy security

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Area of Science:

Computer Science
Cybersecurity
Network Engineering

Background:

The Industrial Internet of Things (IIoT) framework enables remote data analysis via smart sensing devices.
Transmitting real-time IIoT data over public networks poses significant security and privacy risks.
Robust user authentication is essential to prevent unauthorized access in IIoT environments.

Purpose of the Study:

To propose a novel user authentication and key agreement protocol for the IIoT.
To enhance security and functional integrity within IIoT environments.
To restrict service access to authorized users and designated smart sensing devices.

Main Methods:

The protocol integrates cryptographic hash functions, chaotic maps, Physical Unclonable Functions (PUFs), and fuzzy extractors.
PUFs offer protection against tampering and cloning.
Fuzzy extractors enable secure biometric verification using smart cards, passwords, and biometrics.

Main Results:

The protocol supports dynamic device enrollment, password/biometric updates, and smart card revocation.
Formal security analysis using the Real-or-Random (ROR) model validated session key security.
Informal security analysis confirmed resistance to various attacks.

Conclusions:

The proposed protocol offers enhanced security and functional integrity for IIoT systems.
Comparative evaluations show superior efficiency and security compared to existing benchmarks.
This contributes to more secure remote data analysis in industrial settings.