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

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.