Universal Data Anomaly Detection via Inverse Generative Adversary Network

The problem of detecting data anomaly under unknown probability distributions is considered. Whereas the probability distribution of the anomaly-free data is unknown, anomaly-free training samples are assumed to be available. For anomaly data, neither the underlying probability distribution is known nor anomaly data samples are available. A deep learning approach coupled with a statistical test based on coincidence is proposed where an inverse generative adversary network is trained to transform data to the classical uniform vs. nonuniform hypothesis testing problem. The proposed approach is particularly effective to detect persistent anomalies whose distributions have an overlapping domain with that of the anomaly-free distribution.