Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things

• URL: http://arxiv.org/abs/2510.19026v1
• Date: Tue, 21 Oct 2025 19:09:46 GMT
• Title: Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things
• Authors: Behnam Rezaei Bezanjani, Seyyed Hamid Ghafouri, Reza Gholamrezaei,
• Abstract summary: The rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management.<n>These technological innovations raise critical security concerns, particularly in safeguarding medical data against potential cyber threats.<n>We propose a comprehensive method encompassing three distinct phases to address the imperative need for enhanced security in IoT-based healthcare systems.
• Score: 0.5097809301149341
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 the confidentiality, integrity, and availability of patient data in 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 strengthen data transaction security, providing an immutable and transparent framework. In the second phase, we introduce a Request Pattern Recognition Check that leverages diverse data sources to identify and block potential unauthorized access attempts. Finally, the third phase incorporates Feature Selection and a BiLSTM network to enhance the accuracy and efficiency of intrusion detection using advanced machine learning techniques. We compared the simulation results of the proposed method with three recent related methods: AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria include detection rate, false alarm rate, precision, recall, and accuracy - crucial benchmarks for assessing the overall performance of intrusion detection systems. Our findings show that the proposed method outperforms existing approaches across all evaluated criteria, demonstrating its effectiveness in improving the security of IoT-based healthcare systems.

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