Self-Supervised Variational Graph Autoencoder for System-Level Anomaly Detection

Unsupervised anomaly detection (AD) methods, either reconstruction based or prediction based, determine anomalies based on residuals. Occasional mutations in a single variable can cause the residuals to exceed the limits. Indeed, such mutations are not variations in the operating mechanism of the sy...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 72; s. 1 - 11
Hlavní autori:	Zhang, Le, Cheng, Wei, Xing, Ji, Chen, Xuefeng, Nie, Zelin, Zhang, Shuo, Hong, Junying, Xu, Zhao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Anomalies Anomaly detection Anomaly detection (AD) Cognitive tasks Complex systems Correlation Correlation analysis Cross correlation dynamic graphs Feature extraction Graph neural networks Graphs Hyperspheres multitask learning Multitasking Mutation Optimization Personal computers Reconstruction Self-supervised learning Task analysis Time series analysis variational graph autoencoders
ISSN:	0018-9456, 1557-9662
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