Integrating VGG۱۶ with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems

The security of Implantable Medical Devices (IMDs) is of paramount importance, as unauthorized access can lead to life-threatening consequences. Electrocardiogram (ECG) signals present a promising biometric modality for authentication due to their inherent uniqueness and capability for liveness detection capability. However, wireless ECG-based systems are vulnerable to relay attacks, necessitating robust proximity verification. This study proposes a novel, integrated authentication framework that addresses both user identification and physical proximity. We enhance the established Access Control for Implantable Medical Devices (ACIMD) distance-bounding protocol by incorporating a fine-tuned VGG۱۶ deep convolutional neural network to achieve high-accuracy ECG biometric verification. The system was rigorously evaluated using the MIT-BIH Arrhythmia Database. The integrated model achieved an overall authentication accuracy of ۹۹.۴۵%, surpassing the baseline ACIMD protocol accuracy of ۹۷.۸۲%. This represents an average improvement of ۱.۶۳% across various physical distance thresholds. Although integrating VGG۱۶ increased the total authentication time from ۰.۰۱۱۳ s to ۰.۰۴۳۷ s, this remains well within acceptable limits for real-time medical applications. Crucially, we provide new evidence for improved system reliability, demonstrating superior robustness against signal noise compared to the baseline. The proposed system effectively balances high biometric fidelity with stringent physical-layer security, offering a comprehensive solution for secure remote authentication in critical applications such as IMDs.

کلیدواژه ها:

ECG Authentication ، Distance Bounding Protocol ، Remote Telemetry ، ACIMD Protocol ، VGG۱۶ Deep Learning Model