Recent years have seen the emergence of wearable medical systems (WMS) that have demonstrated great promise for improved health monitoring and overall well-being. Ensuring that these WMS accurately monitor a user’s current health state is crucial. This is especially true in the presence of adversaries who want to mount data manipulation attacks on the WMS. The goal of data manipulation attacks is to alter the measurements made by the sensors in the WMS with fictitious data that is plausible but not accurate. Such attacks force clinicians or any decision support system AI, analyzing the WMS data, to make incorrect diagnosis and treatment decisions about the patient’s health. In this paper, we present an approach to detect data manipulation attacks based on the idea that multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) are inherently related to each other. We capture the commonalities between a “target” sensor measurement and another “reference” sensor measurement (which is trustworthy), by building an image reconstruction-based classifier and using this classifier to identify any unilateral changes in the target sensor measurements. This classifier is user-specific and needs to be created for every user on whom the WMS is deployed. In order to showcase our idea, we present a case study where we detect data manipulation attacks on electrocardiogram (ECG) sensor measurements in a WMS using blood pressure measurement as reference. We chose ECG and blood pressure—in arterial blood pressure (ABP) form—because both are some of the most commonly measured physiological signals in a WMS environment. Our approach demonstrates promising results with above 98% accuracy in detecting even subtle ECG alterations for both healthy subjects and those with different cardiac ailments. Finally, we show that the approach is general in that it can be used to build a model for detecting data manipulation attacks that alter ABP sensor measurements using the ECG sensor as reference.

中文翻译：

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在可穿戴医疗系统中检测对生理传感器测量值的数据操纵攻击

近年来，出现了可穿戴医疗系统（WMS），这些系统已显示出改善健康监测和整体健康的巨大希望。确保这些WMS准确监视用户当前的健康状况至关重要。在要在WMS上发起数据操纵攻击的对手的情况下尤其如此。数据操纵攻击的目的是用可信但不准确的虚拟数据来改变WMS中传感器的测量结果。此类攻击迫使临床医生或任何决策支持系统AI分析WMS数据，做出有关患者健康的错误诊断和治疗决策。在本文中，我们提出了一种基于以下思想来检测数据操纵攻击的方法：基于同一基础生理过程（例如心脏过程）的多个生理信号彼此固有地相关。通过构建基于图像重建的分类器并使用此分类器识别目标传感器测量结果中的任何单方面变化，我们捕获了“目标”传感器测量结果与另一“参考”传感器测量结果（值得信赖）之间的共性。该分类器是特定于用户的，需要为部署了WMS的每个用户创建。为了展示我们的想法，我们提出了一个案例研究，其中我们以血压测量为参考，在WMS中检测对心电图（ECG）传感器测量的数据操纵攻击。我们选择ECG和血压（以动脉血压（ABP）形式），因为两者都是WMS环境中最常测量的生理信号。我们的方法显示出可喜的结果，对于健康受试者和患有不同心脏疾病的受试者，即使检测到细微的ECG改变，其准确性也达到98％以上。最后，我们表明该方法是通用的，因为它可用于建立检测数据操纵攻击的模型，这些攻击会使用ECG传感器作为参考来更改ABP传感器的测量值。