Key result
Deep learning ECG authentication secures implantable devices with ~99% accuracy, outperforming existing solutions.
Why the study?
Existing implantable medical device access-control protocols provide only partial protection and face ongoing challenges in accuracy and reliability.
Deep learning-based authentication using ECG signals provides highly accurate and secure access control for implantable medical devices.
Offers a promising cybersecurity approach for implantable devices; leaves open how these algorithms perform.
The Internet of Things in medical applications demands strong privacy and security, particularly for implantable medical devices (IMDs) that must be reconfigured wirelessly through programmer devices. Existing IMD access-control protocols offer partial protection but still face challenges in accuracy and reliability. Biometric signals, especially Electrocardiogram (ECG), provide a promising alternative for secure and continuous authentication in such IoT systems. This work proposes four deep learning-based authorization approaches enabling secure communication between an IMD and a nearby programmer using ECG-derived representations. The methods fall into two categories: (1) Reliable IMD (RI) and unReliable IMD (uRI), where RI assumes the IMD’s internally recorded ECG is trustworthy (authentication uses only the programmer’s signal), while uRI requires cross-verification between IMD and programmer signals; and (2) Omni (multi-user authentication) versus Mono (single-user authentication). Mono-RI and Omni-RI employ convolutional neural network (CNN)-based architectures, whereas Mono-uRI and Omni-uRI use Siamese networks to support both authentication and replay attack detection. Experimental results show that all four models outperform state-of-the-art solutions. Mono-RI achieves 97.73% authentication accuracy, while Omni-uRI reaches 98.75%. Replay attack detection rates are 99.19% for Mono models and 90.73% for Omni models. These findings demonstrate that the proposed methods are effective across different operational scenarios, supporting both high-security modes and emergency modes that demand rapid access, depending on the desired balance between performance and computational complexity. • ECG authentication for medical devices using CNN and Siamese networks. • High accuracy in user verification and replay attack detection. • Balances security and speed for normal and emergency operation modes. • Prototype circuit validates authorization method and energy use.
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A 2025 study studied Implantable medical devices (IMDs) security. Deep learning-based ECG authentication models vs. State-of-the-art solutions was evaluated on Authentication accuracy and replay attack detection rate. Deep learning-based ECG authentication for implantable medical devices achieved up to 98.75% authentication accuracy and 99.19% replay attack detection, outperforming state-of-the-art solutions.
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