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An enhanced spatio-temporal constraints network for anomaly detection in multivariate time series

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成果类型:
期刊论文
作者:
Ge, Di;Dong, Zheng;Cheng, Yuhang;Wu, Yanwen
通讯作者:
Wu, YW
作者机构:
[Wu, Yanwen; Ge, Di] Cent China Normal Univ, Sch Phys Sci & Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
[Dong, Zheng] Beijing Bytedance Technol Co Ltd, 48 Zhichun Rd, Beijing 200000, Peoples R China.
[Cheng, Yuhang] SHAANXI GSXZ Technol Co Ltd, 57 Fengchan Rd, Xian 710061, Shaanxi, Peoples R China.
[Wu, Yanwen] Cent China Normal Univ, Natl Digital Learning Engn Technol Res Ctr, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
通讯机构:
[Wu, YW ] C
Cent China Normal Univ, Sch Phys Sci & Technol, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Multivariate time series;Spatio-temporal modeling;Graph contrastive learning;Unsupervised anomaly detection
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2024
卷:
283
页码:
111169
基金类别:
CRediT authorship contribution statement Di Ge: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Zheng Dong: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing. Yuhang Cheng: Methodology, Project administration. Yanwen Wu: Conceptualization, acquisition, Methodology, Project administration, Writing – review & editing.
机构署名:
本校为第一且通讯机构
院系归属:
物理科学与技术学院
摘要:
Anomaly detection using multivariate time series plays a crucial role in system security. Conventional deep learning detection techniques mainly depend on temporal dependency and employ reconstruction or prediction-based methods. However, as feature variables grow more intricate, there is a risk of neglecting essential spatio-temporal structural information, potentially leading to insufficient model training in unsupervised settings. Hence, we propose an end-to-end anomaly detection model with multiple pre-training tasks designed for the spatio...

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