CSCAD: Correlation Structure-Based Collective Anomaly Detection in Complex System

Detecting anomalies in large complex systems is a critical and challenging task. The difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge for anomalies is hard in real-world systems, which often lead to limited or no anomaly labels in the dataset. Second,...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 5; S. 4634 - 4645
Hauptverfasser:	Qin, Huiling, Zhan, Xianyuan, Zheng, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies Anomaly detection complex system Complex systems Correlation correlation mining Data models Datasets Feature extraction Labels Loss measurement Reconstruction Sensors unsupervised learning urban computing variational autoencoder
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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