Broken Hearted: How To Attack ECG Biometrics
Author(s): Simon Eberz, Nicola Paoletti, Marc Roeschlin, Andrea Patané, Marta Kwiatkowska, Ivan Martinovic
Download: Paper (PDF)
Date: 27 Feb 2017
Document Type: Reports
Associated Event: NDSS Symposium 2017
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. X
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.