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Anomaly detection is facing with emerging challenges in many importantindustry domains, such as cyber security and online recommendation andadvertising. The recent trend in these areas calls for anomaly detection ontime-evolving data with high-dimensional categorical features without labeledsamples. Also, there is an increasing demand for identifying and monitoringirregular patterns at multiple resolutions. In this work, we propose a unifiedend-to-end approach to solve these challenges by combining the advantages ofAdversarial Autoencoder and Recurrent Neural Network. The model learns datarepresentations cross different scales with attention mechanisms, on which anenhanced two-resolution anomaly detector is developed for both instances anddata blocks. Extensive experiments are performed over three types of datasetsto demonstrate the efficacy of our method and its superiority over thestate-of-art approaches.

AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data