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As the Covid-19 pandemic becomes a nationwide problem, physical contact is no longer acceptable. Therefore, biometric technology can be used for practicing social distancing to prevent the spread of the virus. However, face and fingerprint are vulnerable to presentation attacks. Hence alternative modalities such as ECG based biometric become popular. In this paper, we develop a novel presentation attack using a GAN where a short template of the victim's ECG is captured by an attacker and used to generate synthetic fake ECG signals. We also propose a novel framework utilizing residual neural network architecture to analyze ECG presentation attacks.
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Trustworthy of Implantable Medical Devices using ECG Biometric
Implantable medical devices (IMD) such as pacemakers, and cardiac defibrillators are becoming increasingly interconnected to networks for remote patient monitoring. However, networked devices are vulnerable to external attacks that could allow adversaries to gain unauthorized access to devices/ data and break patient privacy. To design a lightweight computational trustworthy of IMD, we propose novel ECG-based biometric authentication using lift and shift method based on post-processing data from the noise generated in an ECG signal recording. The lift and shift method is an ideal addition to this system because it is a quick, lightweight process that produces enough random bits for encrypted communication. ECG is a signal that is already being measured by the IMD, so this ECG biometric could utilize the data that is already being actively recorded. We provide a comprehensive evaluation across multiple NIST tests, as well as ENT and Dieharder statistical suites test
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- Award ID(s):
- 2302084
- PAR ID:
- 10514648
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2157-9
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- San Jose, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SVCC56964.2023.10164853
