Research on University Network Data Anomaly Detection and Security Protection Algorithm Based on Edge Computing

The significance of shielding university network data has improved with the rapid digitalization of the educational process. Distributed security and elevated perimeter defense threats, which place the bigger data center in danger, are the challenges of edge security. This research focuses on creating edge computing (EC) based anomaly detection and security protection that is specially designed for university networks. In this study, we suggested a novel social spider optimization enhanced long-short-term memory (SSO-ELSTM) for attack detection of university network data and security. In the suggested approach for attack detection, we have employed the CTU dataset. The data were preprocessed using Z-score normalization. Then the preprocessed data were used to extract the features for principle component analysis (PCA) using dimensional reduction. The proposed method is implemented using Python software. We compare the suggested method with other existing methods. The outcomes demonstrate that the suggested approach outperforms the other method in terms of accuracy, precision, recall, F1 score and error rate. This study presents a framework for using edge computing and sophisticated anomaly detection methods to enhance university network security; the outcome highlights practicality and efficiency of edge-based solutions for shielding sensitive university network information.