Unsupervised Anomaly Detection in IoT Systems for Smart Cities

Anomaly detection is critical in the Internet of Things (IoT) systems due to its wide applications for building smart cities, such as quality control in manufacturing, intrusion detection in system security, fault detection in system monitoring. Many existing schemes are problem specific and supervi...

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Veröffentlicht in:	IEEE transactions on network science and engineering Jg. 7; H. 4; S. 2231 - 2242
Hauptverfasser:	Guo, Yifan, Ji, Tianxi, Wang, Qianlong, Yu, Lixing, Min, Geyong, Li, Pan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection Bayesian analysis Decoding Fault detection Gated Recurrent Unit (GRU) Gaussian distribution Gaussian Mixture Model (GMM) Image reconstruction Internet of Things iot Quality control Smart cities Statistical inference Time series Time series analysis Training unsupervised anomaly detection Variational Autoencoder(VAE)
ISSN:	2327-4697, 2334-329X
Online-Zugang:	Volltext
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