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Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things

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Abstract

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cyber threats. The sensitive nature of health-related information requires robust measures to ensure patient data's confidentiality, integrity, and availability within IoT-enabled medical environments. Addressing the imperative need for enhanced security in IoT-based healthcare systems, we propose a comprehensive method encompassing three distinct phases. In the first phase, we implement blockchain-enabled request and transaction encryption to fortify the security of data transactions, providing an immutable and transparent framework. Subsequently, in the second phase, we introduce request pattern recognition check, leveraging diverse data sources to identify and thwart potential unauthorized access attempts. Finally, the third phase incorporates feature selection and the BiLSTM network to enhance the accuracy and efficiency of intrusion detection through advanced machine-learning techniques. We compared the simulation results of the proposed method with three recent related methods, namely AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria encompass detection rates, false alarm rates, precision, recall, and accuracy, crucial benchmarks in assessing the overall performance of intrusion detection systems. Notably, our findings reveal that the proposed method outperforms these existing methods across all evaluated criteria, underscoring its superiority in enhancing the security posture of IoT-based healthcare systems.

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No datasets were generated or analyzed during the current study.

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Acknowledgements

Dear Editors-in-Chief, Prof. Hamid R. Arabnia We are grateful to the editors and reviewers for their time and constructive comments on our manuscript: Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things. We have implemented their comments and suggestions and wish to submit a revised version of the manuscript for further consideration in the journal. Changes in the initial version of the manuscript are highlighted for added sentences. Below, we also provide a point-by-point response explaining how we have addressed each of the editor’s or reviewers’ comments. We look forward to the outcome of your assessment. Kind regards, Seyyed Hamid Ghafouri, Ph.D.

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Authors and Affiliations

  1. Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

    Behnam Rezaei Bezanjani, Seyyed Hamid Ghafouri & Reza Gholamrezaei

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  1. Behnam Rezaei Bezanjani
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  2. Seyyed Hamid Ghafouri
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Contributions

Behnam Rezaei Bezanjani contributed to the conceptualization, data curation, investigation, methodology, resources, software, validation, visualization, writing—original draft, and writing—review and editing. Seyyed Hamid Ghafouri was involved in the formal analysis, investigation, methodology, resources, software, supervision, visualization, and writing—review and editing.

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Correspondence to Seyyed Hamid Ghafouri.

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Bezanjani, B.R., Ghafouri, S.H. & Gholamrezaei, R. Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things. J Supercomput 80, 24975–25003 (2024). https://doi.org/10.1007/s11227-024-06392-3

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