Tahir Bushra, Jolfaei Alireza, Tariq Muhammad
IEEE J Biomed Health Inform. 2025 Apr;29(4):2345-2352. doi: 10.1109/JBHI.2023.3236072. Epub 2025 Apr 4.
The Artificial Intelligence-enabled Internet of Medical Things (AI-IoMT) envisions the connectivity of medical devices encompassing advanced computing technologies to empower large-scale intelligent healthcare networks. The AI-IoMT continuously monitors patients' health and vital computations via IoMT sensors with enhanced resource utilization for providing progressive medical care services. However, the security concerns of these autonomous systems against potential threats are still underdeveloped. Since these IoMT sensor networks carry a bulk of sensitive data, they are susceptible to unobservable False Data Injection Attacks (FDIA), thus jeopardizing patients' health. This paper presents a novel threat-defense analysis framework that establishes an experience-driven approach based on a deep deterministic policy gradient to inject false measurements into IoMT sensors, computing vitals, causing patients' health instability. Subsequently, a privacy-preserved and optimized federated intelligent FDIA detector is deployed to detect malicious activity. The proposed method is parallelizable and computationally efficient to work collaboratively in a dynamic domain. Compared to existing techniques, the proposed threat-defense framework is able to thoroughly analyze severe systems' security holes and combats the risk with lower computing cost and high detection accuracy along with preserving the patients' data privacy.
基于人工智能的医疗物联网（AI-IoMT）设想了包含先进计算技术的医疗设备之间的连接，以构建大规模智能医疗网络。AI-IoMT通过物联网传感器持续监测患者的健康状况和重要计算数据，提高资源利用率，以提供渐进式医疗服务。然而，这些自主系统针对潜在威胁的安全问题仍未得到充分发展。由于这些物联网传感器网络承载着大量敏感数据，它们容易受到难以察觉的虚假数据注入攻击（FDIA），从而危及患者的健康。本文提出了一种新颖的威胁防御分析框架，该框架基于深度确定性策略梯度建立了一种经验驱动的方法，向物联网传感器注入虚假测量数据，计算生命体征，导致患者健康不稳定。随后，部署了一个隐私保护且优化的联邦智能FDIA检测器来检测恶意活动。所提出的方法具有可并行性且计算效率高，能够在动态领域中协同工作。与现有技术相比，所提出的威胁防御框架能够全面分析严重的系统安全漏洞，并以较低的计算成本和高检测准确率应对风险，同时保护患者的数据隐私。
