TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder

• Published in: IEEE transactions on network science and engineering Vol. 10; no. 5; pp. 2978 - 2990
• Main Authors: Gao, Honghao, Qiu, Binyang, Barroso, Ramon J. Duran, Hussain, Walayat, Xu, Yueshen, Wang, Xinheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; autoencoder; Complexity theory; Data models; Datasets; Decoding; Feature extraction; Internet of Things; memory augmentation; Modules; Representations; Task analysis; Time series; time-series classification

With the development of communication, the Internet of Things (IoT) has been widely deployed and used in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT increases the data density and the data dimension, where anomaly detection is impo...