Generative adversarial nets-based anomaly detection.

Generative adversarial nets-based anomaly detection.

<p>The features and challenges of different GANs-based anomaly detection methods are listed, highlighting their respective detection effectiveness and shortcomings.</p>

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Bibliographic Details
Main Author:	Xinliang Li (136538) (author)
Other Authors:	Jianmin Peng (20614454) (author), Wenjing Li (198063) (author), Zhiping Song (662315) (author), Xusheng Du (1423195) (author)
Published:	2025
Subjects:	Genetics Molecular Biology Ecology Immunology Developmental Biology Inorganic Chemistry Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified financial fraud identification 34 %, outperforming based anomaly detection unsupervised training process complex data distributions including medical diagnostics detecting anomaly objects local synthetic density gald method achieved local density anomaly objects synthetic data adversarial process data point synthetic neighborhood synthetic dataset world datasets various domains seven real probability distribution primarily fit normal objects material analysis loss function industrial monitoring health monitoring extensive experiments directly affects cybersecurity defense best method average auc
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