Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control
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 environment...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2779 |
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| author | Jibran Saleem Umar Raza Mohammad Hammoudeh William Holderbaum |
| author_facet | Jibran Saleem Umar Raza Mohammad Hammoudeh William Holderbaum |
| author_sort | Jibran Saleem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86128dcb5c0e407c9fd6c1111089bfc6 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-86128dcb5c0e407c9fd6c1111089bfc62025-08-20T03:52:56ZengMDPI AGSensors1424-82202025-04-01259277910.3390/s25092779Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access ControlJibran Saleem0Umar Raza1Mohammad Hammoudeh2William Holderbaum3Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKDepartment of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UKThe 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.https://www.mdpi.com/1424-8220/25/9/2779attribute-based authenticationmachine learninghybrid MLIoTrandom forestsecurity |
| spellingShingle | Jibran Saleem Umar Raza Mohammad Hammoudeh William Holderbaum Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control Sensors attribute-based authentication machine learning hybrid ML IoT random forest security |
| title | Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control |
| title_full | Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control |
| title_fullStr | Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control |
| title_full_unstemmed | Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control |
| title_short | Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control |
| title_sort | machine learning enhanced attribute based authentication for secure iot access control |
| topic | attribute-based authentication machine learning hybrid ML IoT random forest security |
| url | https://www.mdpi.com/1424-8220/25/9/2779 |
| work_keys_str_mv | AT jibransaleem machinelearningenhancedattributebasedauthenticationforsecureiotaccesscontrol AT umarraza machinelearningenhancedattributebasedauthenticationforsecureiotaccesscontrol AT mohammadhammoudeh machinelearningenhancedattributebasedauthenticationforsecureiotaccesscontrol AT williamholderbaum machinelearningenhancedattributebasedauthenticationforsecureiotaccesscontrol |