  Anomaly detection is facing with emerging challenges in many important
industry domains, such as cyber security and online recommendation and
advertising. The recent trend in these areas calls for anomaly detection on
time-evolving data with high-dimensional categorical features without labeled
samples. Also, there is an increasing demand for identifying and monitoring
irregular patterns at multiple resolutions. In this work, we propose a unified
end-to-end approach to solve these challenges by combining the advantages of
Adversarial Autoencoder and Recurrent Neural Network. The model learns data
representations cross different scales with attention mechanisms, on which an
enhanced two-resolution anomaly detector is developed for both instances and
data blocks. Extensive experiments are performed over three types of datasets
to demonstrate the efficacy of our method and its superiority over the
state-of-art approaches.
