A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained o...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2023-04, Vol.34 (4), p.1705-1719
Hauptverfasser:	He, Zilong, Chen, Pengfei, Li, Xiaoyun, Wang, Yongfeng, Yu, Guangba, Chen, Cailin, Li, Xinrui, Zheng, Zibin
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Batch processing Big Data Cloud computing Coders Contamination Data models Deep learning Graph neural networks Long short-term memory Measurement Neural networks Spatiotemporal phenomena Stochasticity System effectiveness Temporal variations Time series analysis Transaction processing unsupervised anomaly detection variational auto-encoder (VAE)
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