Generative adversarial synthetic neighbors-based unsupervised anomaly detection.

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-supervised learning or face challenges with low computational efficiency and poor generalization when dealing with complex distributions. Aim to address these limitations, we introduce a generative adversarial synthetic neighbors-based unsupervised anomaly detection (GASN) method.