  In the context of the Industrial Internet of Things, communication
technology, originally used in home and office environments, is introduced into
industrial applications. Commercial off-the-shelf products, as well as unified
and well-established communication protocols make this technology easy to
integrate and use. Furthermore, productivity is increased in comparison to
classic industrial control by making systems easier to manage, set up and
configure. Unfortunately, most attack surfaces of home and office environments
are introduced into industrial applications as well, which usually have very
few security mechanisms in place. Over the last years, several technologies
tackling that issue have been researched. In this work, machine learning-based
anomaly detection algorithms are employed to find malicious traffic in a
synthetically generated data set of Modbus/TCP communication of a fictitious
industrial scenario. The applied algorithms are Support Vector Machine (SVM),
Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic
data set, supervised learning is possible. Support Vector Machine and k-nearest
neighbour perform well with different data sets, while k-nearest neighbour and
k-means clustering do not perform satisfactorily.
