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An Improved Binomial Distribution-Based Trust Management Algorithm for Remote Patient Monitoring in WBANs

Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
School of Information Technology, Deakin University, Burwood, VIC 3128, Australia
Pakistan Space & Upper Atmosphere Research Commission, Islamabad 44000, Pakistan
Author to whom correspondence should be addressed.
Academic Editor: Ripon Kumar Chakrabortty
Sustainability 2022, 14(7), 3950;
Received: 17 February 2022 / Revised: 20 March 2022 / Accepted: 22 March 2022 / Published: 26 March 2022
The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER. View Full-Text
Keywords: ANN; ECG; advance authentication; WBAN ANN; ECG; advance authentication; WBAN
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MDPI and ACS Style

Rehman, Z.u.; Altaf, S.; Ahmad, S.; Alqahtani, M.; Huda, S.; Iqbal, S. Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network. Sustainability 2022, 14, 3950.

AMA Style

Rehman Zu, Altaf S, Ahmad S, Alqahtani M, Huda S, Iqbal S. Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network. Sustainability. 2022; 14(7):3950.

Chicago/Turabian Style

Rehman, Zia ur, Saud Altaf, Shafiq Ahmad, Mejdal Alqahtani, Shamsul Huda, and Sofia Iqbal. 2022. "Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network" Sustainability 14, no. 7: 3950.

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