  Using a convGRU-based autoencoder, this thesis proposes a framework to learn
spatial-temporal aspects of raw network traffic in an unsupervised and
protocol-agnostic manner. The learned representations are used to measure the
effect on the results of a subsequent anomaly detection and are compared to the
application without the extracted features. The evaluation showed, that the
anomaly detection could not effectively be enhanced when applied on compressed
traffic fragments for the context of network intrusion detection. Yet, the
trained autoencoder successfully generates a compressed representation (code)
of the network traffic, which hold spatial and temporal information. Based on
the models residual loss, the autoencoder is also capable of detecting
anomalies by itself. Lastly, an approach for a kind of model interpretability
(LRP) was investigated in order to identify relevant areas within the raw input
data, which is used to enrich alerts generated by an anomaly detection method.
