  Attacks on industrial enterprises are increasing in number as well as in
effect. Since the introduction of industrial control systems in the 1970's,
industrial networks have been the target of malicious actors. More recently,
the political and warfare-aspects of attacks on industrial and critical
infrastructure are becoming more relevant. In contrast to classic home and
office IT systems, industrial IT, so-called OT systems, have an effect on the
physical world. Furthermore, industrial devices have long operation times,
sometimes several decades. Updates and fixes are tedious and often not
possible. The threats on industry with the legacy requirements of industrial
environments creates the need for efficient intrusion detection that can be
integrated into existing systems. In this work, the network data containing
industrial operation is analysed with machine learning- and time series- based
anomaly detection algorithms in order to discover the attacks introduced to the
data. Two different data sets are used, one Modbus-based gas pipeline control
traffic and one OPC UA-based batch processing traffic. In order to detect
attacks, two machine learning-based algorithms are used, namely \textit{SVM}
and Random Forest. Both perform well, with Random Forest slightly outperforming
SVM. Furthermore, extracting and selecting features as well as handling missing
data is addressed in this work.
