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.

The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.