Broken Hearted: How To Attack ECG Biometrics

Abstract

In this work we present a systematic presentation attack against ECG biometrics. We demonstrate the attack's effectiveness using the Nymi Band, a wrist band that uses electrocardiography (ECG) as a biometric to authenticate the wearer. We instantiate the attack using a hardware-based Arbitrary Waveform Generator (AWG), an AWG software using a computer sound card, and the playback of ECG signals encoded as .wav files using an off-the-shelf audio player. In two sets of experiments we collect data from a total of 41 participants using a variety of ECG monitors, including a medical monitor, a smartphone-based mobile monitor and the Nymi Band itself. We use the first dataset to understand the statistical differences in biometric features that arise from using different measurement devices and modes. Such differences are addressed through the automated derivation of so-called mapping functions, whose purpose is to transform ECG signals from any device in order to resemble the morphology of the signals recorded with the Nymi Band. As part of our second dataset, we enroll users into the Nymi Band and test whether data from any of our sources can be used for a signal injection attack. Using data collected directly on the Nymi Band we achieve a success rate of 81%. When only using data gathered on other devices, this rate decreases to 43% when using raw data, and 62% after applying the mapping function. While we demonstrate the attack on the Nymi Band, we expect other ECG-based authentication systems to most likely suffer from the same, fundamental weaknesses. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

When Your Fitness Tracker Betrays You: Quantifying the Predictability of Biometric Features Across Contexts

“…Reports show that large amounts of sensitive healthcare data are vulnerable to leakage or theft, or have already been compromised because of security lapses at hospitals, insurance companies or government agencies [26]. Adversaries may also easily obtain raw ECG signal from photos of ECG printouts, as has been shown in previous work [2].…”

“…Earlier work authenticates users while they are being shown controlled stimuli, such as images [13] or moving shapes [14]. Eberz [19] Touch dynamics Assisted manual imitation Perfect [20] Touch dynamics Automatic (robot) None [21] Touch dynamics Automatic (robot) Perfect [22] Gait Assisted manual imitation Perfect [2] ECG Signal generator Cross-device users while they perform standard computer tasks (reading, typing, browsing and watching videos) [15]. Their featureset consists of temporal features reflecting short-term speed and acceleration, spatial features that measure the steadiness of the gaze and the changes of the pupil diameter.…”

“…However, assuming the actual template is stored securely, this may not always be a safe assumption as the attacker may only be able to sample the victim's biometric in a different context (e.g., different device or environment). Eberz et al demonstrate an attack on ECG biometrics that considers different sources of information for the attacker, including ehealth and medical devices [2]. The victim's data is injected into the authentication system by using a standard audio player as a signal generator.…”

“…• How does this predictability impact the security of biometric authentication? To answer these questions, we generalize the methodology of [2] to formalize an approach to automatically derive a cross-context feature mapping based on population data. The mapping then enables us to score both individual features and biometrics with regard to their predictability across contexts.…”

Synthesizing stealthy reprogramming attacks on cardiac devices

“…Rather, they simply showed that they can disable all therapies, an attack which is easily detectable. Other examples are spoofing attacks on ICDs [16], attacks on insulin pumps and glucose monitors [18], and on electrocardiogram-based biometrics [6]. The work of Jiang et al [14] offers a model-based approach to analyze the accuracy ICD algorithms by conducting in-silico trials on synthetic cardiac signals.…”