Generative adversarial synthetic neighbors-based unsupervised anomaly detection

Generative adversarial synthetic neighbors-based unsupervised anomaly detection

Abstract Anomaly detection is crucial for the stable operation of mechanical systems, securing financial transactions, and ensuring network security, among other critical areas. Presently, Generative Adversarial Networks (GANs)-based anomaly detection methods either require labeled data for semi-sup...

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Bibliographic Details
Main Authors: Lan Chen, Hong Jiang, Lizhong Wang, Jun Li, Manhua Yu, Yong Shen, Xusheng Du
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Anomaly Detection
Generative adversarial networks
Nearest neighbor methods
Bearing Fault
Data distribution
Online Access:https://doi.org/10.1038/s41598-024-84863-6
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