Detecting malicious temporal alterations of ECG signals in body sensor networks

Abstract

Electrocardiogram (ECG) sensor is one of the most commonly available and medically important sensors in a Body Sensor Network (BSN). Compromise of the ECG sensor can have severe consequences for the user as it monitors the user’s cardiac process. In this paper, we propose an approach called SIgnal Feature-correlation-based Testing (SIFT) which is used to detect temporal alteration of ECG sensors in a BSN. The novelty of SIFT lies in the fact that it does not require redundant ECG sensors nor the subject’s historical ECG data to detect the temporal alteration. SIFT works by leveraging multiple physiological signals based on the same underlying physiological process (e.g., cardiac process) – arterial blood pressure and respiration. Analysis of our case study demonstrates promising results with ∼98% accuracy in detecting even subtle alterations in the temporal properties of an ECG signal.