The swift integration of intelligent technologies at higher institutions of learning has greatly improved efficiency of operations, learning conditions, and administration. But the evolution of cybersecurity issues presented by the integration of Internet of Things (IoT) devices, cloud-based systems, and interconnected systems has complicated and shifted the complexity of these issues, which old and standard methods of periodic auditing cannot effectively tackle. The present paper suggests an AI-based Cyber Risk Auditing Framework (AI-CRAF) of continuous and real-time risk assessment in smart educational campuses. The framework combines sophisticated machine learning and deep learning models to identify the threats, anomalies, and dynamic risk assessment with references to vulnerabilities to the system and their potential impact. The suggested model is tested on a big data set of 1,247,334 events within 12 months that contains various attack cases and regular operations. The experimental values indicate a high detection accuracy of 96.2 %, a true detection rate of 94.8 %, a low false positive rate of 2.1 % and an AUC-ROC value of 0.978. Also, the framework shortens 97.1 the time spent on an average incident response by 41.9 minutes to 1.2 minutes as compared to conventional methods.
Alhumaid et al. (Thu,) studied this question.