The COVID-19 pandemic made all organizations and enterprises work on cloud platforms from home, which greatly facilitates cyberattacks. Employees who work remotely and use cloud-based platforms are chosen as targets for cyberattacks. For that reason, cyber security is a more concerning issue and is now incorporated into almost every smart gadget and has become a prerequisite in every software product and service. There are various mitigations for external cyber security attacks, but hardly any for insider security threats, as they are difficult to detect and mitigate. Thus, insider cyber security threat detection has become a serious concern in recent years. Hence, this paper proposes an unsupervised deep learning approach that employs an artificial neural network (ANN)-based autoencoder to detect anomalies in an insider cyber security attack scenario. The proposed approach analyzes the behavior of the patterns of users and machines for anomalies and sends an alert based on a set security threshold. The threshold value set for security detection is calculated based on reconstruction errors that are obtained through testing the normal data. When the proposed model reconstructs the user behavior without generating sufficient reconstruction errors, i.e., no more than the threshold, the user is flagged as normal; otherwise, it is flagged as a security intruder. The proposed approach performed well, with an accuracy of 94.3% for security threat detection, a false positive rate of 11.1%, and a precision of 89.1%. From the obtained experimental results, it was found that the proposed method for insider security threat detection outperforms the existing methods in terms of performance reliability, due to implementation of ANN-based autoencoder which uses a larger number of features in the process of security threat detection.

In the past decades, AI was not playing a pivotal role in technology. But now, artificial intelligence is a key part of the revolution of technology. AI has become a powerful tool in recent years, enabling machines to think and act like humans. AI helps in developing a better approach to teaching students (i.e., adaptive learning where a computer algorithm identifies the rate of learning of a student and adapts itself so that the student learns efficiently). The aim of implementing AI in education is not to replace tutors, but to assist them in understanding the potential and limitations of each student individually. This chapter will cover the introduction, existing applications, and new applications of artificial intelligence in higher education. The prime focus of the chapter will be on the approaches to different product developments and their implementation using artificial intelligence in the education sector. It provides a brief idea of how the existing applications solve the problems in higher education and new approaches to make higher education more efficient.