Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

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.