Abstract
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.
Highlights
Emerging wearable medical systems (WMS) are revolutionizing the way of seeking and delivering healthcare
We present a case study that focuses on two physiological signals—ECG and blood pressure—in its arterial blood pressure (ABP) form
In [15], we developed an ECG temporal alteration detection model, which captures the alterations of the timing properties (RR interval) of the ECG signal by correlating ABP and respiration signals with the ECG signal, while, in [16], we developed an ECG morphological alteration detection model that detected a change in the shape of the ECG signal as a result of data manipulation attacks using only the ABP signal as reference
Summary
Emerging wearable medical systems (WMS) are revolutionizing the way of seeking and delivering healthcare. It consists of a number of wireless sensing devices ( referred to as sensors) which form a distributed wireless network [1] around the body of the person who wears them (i.e., the user). An R-peak in the ECG signal will typically be followed by a systolic peak in the ABP signal (see Fig. 4) This is because both represent the compression of the ventricles that results in the blood being circulated through the entire body. Any pathologies in the cardiac process that results in an abnormal ECG wave form will be reflected in the ABP signal [29] This final observation forms the basis of our data manipulation detector
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