Research Article
On the Trend and Problems of IoT Data Anomaly Detection
@INPROCEEDINGS{10.1007/978-3-031-50580-5_31, author={Shuai Li and Lejie Li and Kaining Xu and Jiafeng Yang and Siying Qu}, title={On the Trend and Problems of IoT Data Anomaly Detection}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Internet of Things Anomaly detection Trend}, doi={10.1007/978-3-031-50580-5_31} }- Shuai Li
Lejie Li
Kaining Xu
Jiafeng Yang
Siying Qu
Year: 2024
On the Trend and Problems of IoT Data Anomaly Detection
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_31
Abstract
With the rapid development of Internet technology, the Internet of Things is also constantly developing and progressing. More and more areas are starting to see connected devices, and more and more data is being generated by them. Effective data analysis and detection can prevent network intrusions and predict future trends. In recent years, with the breakthrough of computer technology, machine learning has shown good results in anomaly detection. Therefore, the research on anomaly detection of Internet of Things data has gradually increased and deepened. This work analyzes and summarizes the research trends in this field. First, we use keyword search to export articles in this field. Then we use the tool bibliometrix to generate statistical charts and trend charts for exported articles. At last, we analyze and summarize the generated two graphs. In the process of analysis, we have a detailed description of the phenomenon and a cause analysis. Finally, the future research direction in this field is derived.
