The rapid growth of Internet of Things (IoT) devices across industrial and critical sectors requires robust and efficient authentication mechanisms. Traditional authentication systems struggle to balance security, privacy and computational efficiency, particularly in resource-constrained environments such as Industry 4.0. This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. The SmartIoT Hybrid ML Model utilises Random Forest classifiers for real-time anomaly detection, dynamically assessing access requests based on user attributes, login patterns and behavioural analysis. The model enhances identity protection while enabling secure authentication without exposing sensitive information by incorporating privacy-preserving Attribute-Based Credentials and Attribute-Based Signatures. Our experimental evaluation demonstrates 86% authentication accuracy, 88% precision and 96% recall, significantly outperforming existing solutions while maintaining an average response time of 112ms, making it suitable for low-power IoT devices. Comparative analysis with state-of-the-art authentication frameworks shows the model’s security resilience, computational efficiency and adaptability in real-world IoT applications.

The increasing deployment of intelligent cyber-physical systems (CPS), autonomous devices, and IoT infrastructures in Industry 4.0 has introduced complex and dynamic security challenges that static, identity-based mechanisms are no longer sufficient to address. Intelligent industrial environments demand security frameworks capable of fine-grained, context-aware, and adaptive access control that can operate at scale across heterogeneous networked systems. In response to this need, Attribute-Based Encryption (ABE) and Attribute-Based Authentication (ABA) have emerged as essential building blocks for intelligent security architectures, enabling policy-driven data protection and authentication that are inherently aligned with the dynamic, attribute-rich nature of Industry 4.0 systems. This paper presents a structured review of ABE and ABA schemes, analysing key variants including KP-ABE, CP-ABE, and Decentralised ABE with respect to security, computational efficiency, and suitability for AI-augmented access control in Industry 4.0. Real-world deployments and future research directions, including AI-driven dynamic policy adaptation and energy-efficient schemes for intelligent edge devices, are also examined. The findings advance the understanding of ABE and ABA as core intelligent security control components, supporting the design of robust and autonomously adaptive security systems for next-generation industrial environments.