MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learning

MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learning

Abstract As the cloud services market grows, cloud management tools that detect network anomalies in a non-intrusive manner are critical to improve users’ experience of cloud services. However, some network anomalies, such as Microburst, in cloud systems are very discreet. Network monitoring methods...

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Bibliographic Details
Main Authors: Yunfeng Duan, Chenxu Li, Guotao Bai, Guo Chen, Fanqin Zhou, Jiaxing Chen, Zehua Gao, Chun Zhang
Format: Article
Language:English
Published: SpringerOpen 2023-08-01
Series:Journal of Cloud Computing: Advances, Systems and Applications
Subjects:
Anomaly detection
Time series analysis
In-band network telemetry
Deep learning
Data stream mining
Cloud computing
Online Access:https://doi.org/10.1186/s13677-023-00492-w

Internet

https://doi.org/10.1186/s13677-023-00492-w

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