IEEE J Biomed Health Inform. 2023 Feb;27(2):710-721. doi: 10.1109/JBHI.2022.3187037. Epub 2023 Feb 3.
The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.
物联网医疗（IoMT）已成为医疗领域的重要支撑，它能够拓宽用户体验，实现近实时远程诊断等关键解决方案，从而提高生活质量。然而，在安全领域，隐私和安全问题仍然没有得到很好的解决。已经考虑了各种基于规则的方法来识别 IoMT 中的异常行为，并在适用于轻量级物联网设备的情况下展示了较高的异常行为检测准确性。然而，这些解决方案大多存在隐私问题，尤其是在进行异常行为分析时提供上下文信息时。此外，伪造或修改上下文会产生高比例的误报，有时甚至会导致异常行为检测绕过。基于最近区块链和联邦学习（FL）的强大融合，我们提出了一种用于轻量级 IoMT 设备中安全异常行为检测的高效隐私保护框架，特别是在人工胰腺系统（APS）中。该方法采用隐私保护的双向长短时记忆网络（BiLSTM），并通过基于以太坊智能合约环境的区块链技术集成来增强安全性。所提出的模型在可持续隐私保护、具有不可追踪性的相称激励方案、全面性以及变体神经网络方法的紧凑结果等方面进行了实证基准测试。结果表明，该模型的召回率达到了 99.93%，几乎可以检测到目标用例中的所有恶意事件。此外，在初始以太值为 100 的情况下，该解决方案的平均气体消耗和消耗的以太值分别为 84,456.5 和 0.03157625。
