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Updated: May 17, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
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Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control.

Jibran Saleem1, Umar Raza1, Mohammad Hammoudeh2

  • 1Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the SmartIoT Hybrid Machine Learning (ML) Model for secure Internet of Things (IoT) authentication. It enhances security and efficiency in Industry 4.0 environments using attribute-based methods and ML anomaly detection.

Keywords:
Industry 4.0IoTattribute-based authenticationhybrid MLmachine learningrandom forestsecurity

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Rapid growth of Internet of Things (IoT) devices necessitates advanced authentication.
  • Traditional systems face challenges in balancing security, privacy, and efficiency in resource-constrained environments like Industry 4.0.
  • Existing solutions often struggle with computational overhead and protecting sensitive information.

Purpose of the Study:

  • To present the SmartIoT Hybrid Machine Learning (ML) Model for enhanced IoT authentication.
  • To improve security and minimize computational overhead in authentication mechanisms.
  • To provide a solution suitable for low-power IoT devices and Industry 4.0 applications.

Main Methods:

  • Integration of Attribute-Based Authentication with a lightweight machine learning algorithm.
  • Utilization of Random Forest classifiers for real-time anomaly detection based on user attributes, login patterns, and behavioral analysis.
  • Incorporation of privacy-preserving Attribute-Based Credentials and Attribute-Based Signatures.

Main Results:

  • Achieved 86% authentication accuracy, 88% precision, and 96% recall.
  • Demonstrated an average response time of 112ms, suitable for low-power IoT devices.
  • Significantly outperformed existing solutions in experimental evaluations.

Conclusions:

  • The SmartIoT Hybrid ML Model offers enhanced security, privacy, and computational efficiency for IoT authentication.
  • The model exhibits strong security resilience, efficiency, and adaptability for real-world applications.
  • It provides a viable solution for securing critical sectors and Industry 4.0 environments.