  The problem of detecting data anomaly is considered. Under the null
hypothesis that models anomaly-free data, measurements are assumed to be from
an unknown distribution with some authenticated historical samples. Under the
composite alternative hypothesis, measurements are from an unknown distribution
positive distance away from the distribution under the null hypothesis. No
training data are available for the distribution of anomaly data. A
semi-supervised deep learning technique based on an inverse generative
adversary network is proposed.
