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.

翻译：近年来，物联网设备在医疗领域的快速融合为患者护理与数据管理带来了革命性进步。尽管这些技术创新前景广阔，它们也同时引发了关键的安全隐患，尤其是在保护医疗数据免受潜在网络威胁方面。健康相关信息的敏感性要求采取强有力措施，以确保物联网医疗环境中患者数据的机密性、完整性与可用性。为应对基于物联网的医疗系统对增强安全性的迫切需求，我们提出了一种包含三个不同阶段的综合方法。在第一阶段，我们实施基于区块链的请求与交易加密以加强数据交易安全，提供一个不可篡改且透明的框架。第二阶段引入请求模式识别检查，利用多样化数据源识别并拦截潜在的未授权访问尝试。最后，第三阶段结合特征选择与BiLSTM网络，通过先进的机器学习技术提升入侵检测的准确性与效率。我们将所提方法的仿真结果与三种近期相关方法——AIBPSF-IoMT、OMLIDS-PBIoT及AIMMFIDS——进行了比较。评估标准包括检测率、误报率、精确率、召回率与准确率，这些是评估入侵检测系统整体性能的关键基准。研究结果表明，所提方法在所有评估标准上均优于现有方法，证明了其在提升基于物联网的医疗系统安全性方面的有效性。