Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.
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