  Anomalies represent deviations from the intended system operation and can
lead to decreased efficiency as well as partial or complete system failure. As
the causes of anomalies are often unknown due to complex system dynamics,
efficient anomaly detection is necessary. Conventional detection approaches
rely on statistical and time-invariant methods that fail to address the complex
and dynamic nature of anomalies. With advances in artificial intelligence and
increasing importance for anomaly detection and prevention in various domains,
artificial neural network approaches enable the detection of more complex
anomaly types while considering temporal and contextual characteristics. In
this article, a survey on state-of-the-art anomaly detection using deep neural
and especially long short-term memory networks is conducted. The investigated
approaches are evaluated based on the application scenario, data and anomaly
types as well as further metrics. To highlight the potential of upcoming
anomaly detection techniques, graph-based and transfer learning approaches are
also included in the survey, enabling the analysis of heterogeneous data as
well as compensating for its shortage and improving the handling of dynamic
processes.
