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

Abstract

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 from counselling 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.