  The increasing interaction of industrial control systems (ICSs) with public
networks and digital devices introduces new cyber threats to power systems and
other critical infrastructure. Recent cyber-physical attacks such as Stuxnet
and Irongate revealed unexpected ICS vulnerabilities and a need for improved
security measures. Intrusion detection systems constitute a key security
technology, which typically monitors cyber network data for detecting malicious
activities. However, a central characteristic of modern ICSs is the increasing
interdependency of physical and cyber network processes. Thus, the integration
of network and physical process data is seen as a promising approach to improve
predictability in real-time intrusion detection for ICSs by accounting for
physical constraints and underlying process patterns. This work systematically
assesses machine learning-based cyber-physical intrusion detection and
multi-class classification through a comparison to its purely network
data-based counterpart and evaluation of misclassifications and detection
delay. Multiple supervised detection and classification pipelines are applied
on a recent cyber-physical dataset, which describes various cyber attacks and
physical faults on a generic ICS. A key finding is that the integration of
physical process data improves detection and classification of all considered
attack types. In addition, it enables simultaneous processing of attacks and
faults, paving the way for holistic cross-domain root cause identification.
