Deep learning-based anomaly detection method.

<p>The capabilities and limitations of deep learning methods in dealing with complex data distributions are compared.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Xinliang Li (136538) (author)
مؤلفون آخرون:	Jianmin Peng (20614454) (author), Wenjing Li (198063) (author), Zhiping Song (662315) (author), Xusheng Du (1423195) (author)
منشور في:	2025
الموضوعات:	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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