Challenge of Anomaly Detection in IoT Analytics
Hao-Ting Pai, Szu‐Hong Wang, Tsung‐Sheng Chang, Jian‐Xing Wu · 2020
Many studies applied anomaly detection technology to varied areas such as fraud detection for finance activities, fault detection in industrial systems, and so on. However, big data rises to the challenge of performing a large scale of anomaly analytics in IoT. In this paper, we adopt several methods to analyze a realworld dataset on anomalous events in paper and pulp industry. By experiments, we discuss the findings and illustrate the difficulty in identifying anomalies, which provide useful information for further study.