Open Access

ARTICLE

Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

Abdullah Saad AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*

1 Information Systems Department, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Centre of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City, 11884, Cairo, Egypt

* Corresponding Author: Mahmoud Ragab. Email:

Computers, Materials & Continua 2022, 72(2), 3131-3144. https://doi.org/10.32604/cmc.2022.026457

Received 27 December 2021; Accepted 11 February 2022; Issue published 29 March 2022

Abstract

Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint biometrics. Besides, the SRODL-LAC technique designs a median filtering (MF) based preprocessing approach to improving the quality of the image. In addition, the Densely Connected Networks (DenseNet-77) model is applied for the extraction of features. Moreover, search and rescue optimization (SRO) algorithm with deep neural network (DNN) model is utilized for the classification process. Lastly, template matching process is done for fingerprint identification. A wide range of simulation analyses is carried out and the results are inspected under several aspects. The experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

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Keywords

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