Next Article in Journal
Shedding Light into the Need of Knowledge Sharing in H2020 Thematic Networks for the Agriculture and Forestry Innovation
Next Article in Special Issue
A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
Previous Article in Journal
Sustainable Development Directions for Wine Tourism in Douro Wine Region, Portugal
Previous Article in Special Issue
An Improved Binomial Distribution-Based Trust Management Algorithm for Remote Patient Monitoring in WBANs
 
 
Article

Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

1
University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
2
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
3
School of Information Technology, Deakin University, Burwood, VIC 3128, Australia
4
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; https://doi.org/10.3390/su14073950
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
Show Figures

Figure 1

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. https://doi.org/10.3390/su14073950

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. https://doi.org/10.3390/su14073950

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. https://doi.org/10.3390/su14073950

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop