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Eshaghi, Narges, Habibi, Mohammad, Bagheri, Nasour, Khatibi, Ali. (1405). Integrating VGG16 with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems. , 13(1), 23-34. doi: 10.22108/jcs.2026.145712.1173
Narges Eshaghi; Mohammad Habibi; Nasour Bagheri; Ali Khatibi. "Integrating VGG16 with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems". , 13, 1, 1405, 23-34. doi: 10.22108/jcs.2026.145712.1173
Eshaghi, Narges, Habibi, Mohammad, Bagheri, Nasour, Khatibi, Ali. (1405). 'Integrating VGG16 with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems', , 13(1), pp. 23-34. doi: 10.22108/jcs.2026.145712.1173
Eshaghi, Narges, Habibi, Mohammad, Bagheri, Nasour, Khatibi, Ali. Integrating VGG16 with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems. , 1405; 13(1): 23-34. doi: 10.22108/jcs.2026.145712.1173

Integrating VGG16 with the ACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems

Journal of Computing and Security
مقاله 3، دوره 13، شماره 1، فروردین 2026، صفحه 23-34    اصل مقاله (2.54 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2026.145712.1173
نویسندگان
Narges Eshaghi1؛ Mohammad Habibi* 1؛ Nasour Bagheri2؛ Ali Khatibi1
1Department of Mathematics, Tafresh University, Iran.
2CPS2 Lab, Department of Communication, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran. Electronics Research Institute, Sharif University of Technology, Tehran, Iran.
چکیده
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 VGG16 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 99.45%, surpassing the baseline ACIMD protocol accuracy of 97.82%. This represents an average improvement of 1.63% across various physical distance thresholds. Although integrating VGG16 increased the total authentication time from 0.0113 s to 0.0437 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.
کلیدواژه‌ها
ECG Authentication؛ Distance Bounding Protocol؛ Remote Telemetry؛ ACIMD Protocol؛ VGG16 Deep Learning Model
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