Research Article
Research on Anomaly Detection of Distributed Intelligent Teaching System Based on Cloud Computing
@INPROCEEDINGS{10.1007/978-3-031-21161-4_54, author={Fayue Zheng and Lei Ma and Hongxue Yang and Leiguang Liu}, title={Research on Anomaly Detection of Distributed Intelligent Teaching System Based on Cloud Computing}, proceedings={e-Learning, e-Education, and Online Training. 8th EAI International Conference, eLEOT 2022, Harbin, China, July 9--10, 2022, Proceedings, Part I}, proceedings_a={ELEOT}, year={2023}, month={3}, keywords={Cloud computing Distributed Intelligent teaching Anomaly detection}, doi={10.1007/978-3-031-21161-4_54} }- Fayue Zheng
Lei Ma
Hongxue Yang
Leiguang Liu
Year: 2023
Research on Anomaly Detection of Distributed Intelligent Teaching System Based on Cloud Computing
ELEOT
Springer
DOI: 10.1007/978-3-031-21161-4_54
Abstract
The traditional anomaly detection method of intelligent teaching system has some problems, such as poor accuracy and response efficiency. Therefore, this paper proposes a distributed anomaly detection method of intelligent teaching system based on cloud computing. Collect the abnormal data of distributed intelligent teaching system through cloud computing method, calculate the local reachable density according to Gaussian distribution function, build a data management model, and use distributed technology to locate and manage the abnormal area of teaching data, so as to achieve the goal of data detection and identification. The experimental results show that this method can effectively improve the recall rate of anomaly detection data in intelligent teaching system, and the response efficiency has been effectively improved.
