Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

Zia ur Rehman, Saud Altaf, Shafiq Ahmad, Mejdal Alqahtani, Shamsul Huda and Sofia Iqbal
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Zia ur Rehman: University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
Shafiq Ahmad: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Mejdal Alqahtani: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Shamsul Huda: School of Information Technology, Deakin University, Burwood, VIC 3128, Australia
Sofia Iqbal: Pakistan Space & Upper Atmosphere Research Commission, Islamabad 44000, Pakistan

Sustainability, 2022, vol. 14, issue 7, 1-14

Abstract: 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.

Keywords: ANN; ECG; advance authentication; WBAN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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