  In order to introduce deep learning technologies into anomaly detection,
Generative Adversarial Networks (GANs) are considered as important roles in the
algorithm design and realistic applications. In terms of GANs, event
probability reflected in the objective function, has an impact on the event
generation which plays a crucial part in GAN-based anomaly detection. The
information metric, e.g. Kullback-Leibler divergence in the original GAN, makes
the objective function have different sensitivity on different event
probability, which provides an opportunity to refine GAN-based anomaly
detection by influencing data generation. In this paper, we introduce the
exponential information metric into the GAN, referred to as MIM-based GAN,
whose superior characteristics on data generation are discussed in theory.
Furthermore, we propose an anomaly detection method with MIM-based GAN, as well
as explain its principle for the unsupervised learning case from the viewpoint
of probability event generation. Since this method is promising to detect
anomalies in Internet of Things (IoT), such as environmental, medical and
biochemical outliers, we make use of several datasets from the online ODDS
repository to evaluate its performance and compare it with other methods.
