Hybrid Blockchain–Machine Learning Framework for Secure and Intelligent Intrusion Detection in Industrial Internet of Things

Zaid Mohammed Mortada; Ola Baqer Abdulhadi

Authors

Zaid Mohammed Mortada Department of Postgraduate Studies, University of Kufa, Najaf, Iraq
Ola Baqer Abdulhadi Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq

Keywords:

Blockchain, Intrusion Detection System (IDS), Machine Learning, Optimization Algorithm

Abstract

As a result of the Industrial Internet of Things (IIoT), the industrial ecosystem has been transformed by the exchange of data in real-time and the automation of operations in an intelligent manner. Cybersecurity risks are associated with this connectivity, especially for resource-constrained and heterogeneous IIoT networks. An intrusion detection framework incorporating blockchain technology and machine learning algorithms is proposed in this study to address these challenges. Data sharing and auditability are ensured through decentralized, tamper-resistant blockchain, while evolving threats can be detected through adaptive machine learning. For multiclass classification, the proposed system takes advantage of XGBoost, and for hyperparameter optimization, it uses the HAFSO algorithm. For multiclass classification, the proposed system takes advantage of XGBoost, and for hyperparameter optimization, it uses the HAFSO algorithm.

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