Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

Education acts as an important part of economic growth and improvement in human welfare.The educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education field.Almost every action in university and college,right from the process fromcounselling to admissions and fee deposits has been automated.Attendance records,quiz,evaluation,mark,and grade submissions involved the utilization of the ICT.Therefore,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for CyberSecurity in Higher Education Institutions(AOD-CSHEI)technique.The AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the HEIs.The AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class labelling.In addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the data.Besides,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs network.Finally,the SSA is utilized to effectually adjust the hyper parameters of the DNN approach.In order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

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 SRODLLAC technique.The proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint biometrics.Besides,the SRODLLACtechnique 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.