  Machine learning on graph-structured data has recently become a major topic
in industry and research, finding many exciting applications such as
recommender systems and automated theorem proving. We propose an energy-based
graph embedding algorithm to characterize industrial automation systems,
integrating knowledge from different domains like industrial automation,
communications and cybersecurity. By combining knowledge from multiple domains,
the learned model is capable of making context-aware predictions regarding
novel system events and can be used to evaluate the severity of anomalies that
might be indicative of, e.g., cybersecurity breaches. The presented model is
mappable to a biologically-inspired neural architecture, serving as a first
bridge between graph embedding methods and neuromorphic computing - uncovering
a promising edge application for this upcoming technology.
