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IntegratingVGG16 with theACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems.

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Τίτλος: IntegratingVGG16 with theACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems.
Συγγραφείς: Eshaghi, Narges, Habibi, Mohammad, Bagheri, Nasour, Khatibi, Ali
Πηγή: Journal of Computing & Security; Apr2026, Vol. 13 Issue 1, p23-34, 12p
Θεματικοί όροι: Biometric identification, Convolutional neural networks, Biometry, Artificial implants, Electronic authentication, Reliability in engineering
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computing & Security is the property of University of Isfahan and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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PubType: Academic Journal
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  Data: IntegratingVGG16 with theACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems.
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  Data: Journal of Computing & Security; Apr2026, Vol. 13 Issue 1, p23-34, 12p
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  Data: <searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+implants%22">Artificial implants</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+authentication%22">Electronic authentication</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computing & Security is the property of University of Isfahan and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.22108/jcs.2026.145712.1173
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 23
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      – SubjectFull: Biometric identification
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Biometry
        Type: general
      – SubjectFull: Artificial implants
        Type: general
      – SubjectFull: Electronic authentication
        Type: general
      – SubjectFull: Reliability in engineering
        Type: general
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      – TitleFull: IntegratingVGG16 with theACIMD Protocol to Enhance Security and Reliability in ECG-Based Remote Authentication Systems.
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            NameFull: Habibi, Mohammad
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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