  Automatic log file analysis enables early detection of relevant incidents
such as system failures. In particular, self-learning anomaly detection
techniques capture patterns in log data and subsequently report unexpected log
event occurrences to system operators without the need to provide or manually
model anomalous scenarios in advance. Recently, an increasing number of
approaches leveraging deep learning neural networks for this purpose have been
presented. These approaches have demonstrated superior detection performance in
comparison to conventional machine learning techniques and simultaneously
resolve issues with unstable data formats. However, there exist many different
architectures for deep learning and it is non-trivial to encode raw and
unstructured log data to be analyzed by neural networks. We therefore carry out
a systematic literature review that provides an overview of deployed models,
data pre-processing mechanisms, anomaly detection techniques, and evaluations.
The survey does not quantitatively compare existing approaches but instead aims
to help readers understand relevant aspects of different model architectures
and emphasizes open issues for future work.
