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.

本研究提出了一种基于能量的图嵌入算法来描述工业自动化系统，学习多个领域的知识，从而可以进行上下文感知预测和评估异常严重性，该模型可以应用于机器学习和神经形态计算等领域。