  Anomaly detection is an important problem that has been well-studied within
diverse research areas and application domains. The aim of this survey is
two-fold, firstly we present a structured and comprehensive overview of
research methods in deep learning-based anomaly detection. Furthermore, we
review the adoption of these methods for anomaly across various application
domains and assess their effectiveness. We have grouped state-of-the-art
research techniques into different categories based on the underlying
assumptions and approach adopted. Within each category we outline the basic
anomaly detection technique, along with its variants and present key
assumptions, to differentiate between normal and anomalous behavior. For each
category, we present we also present the advantages and limitations and discuss
the computational complexity of the techniques in real application domains.
Finally, we outline open issues in research and challenges faced while adopting
these techniques.
