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.

Pot experiments were performed to study the effects of abscisic acid (ABA) and melatonin (MT) on cotton drought tolerance and to explore their combined effects. ABA or MT spraying promoted water status and antioxidant capacity of drought-stressed leaves, which was conducive to scavenge ROS, finally increasing lint yield. However, the mitigation mechanisms of ABA and MT on drought were not identical, which were mainly manifested as: (1) ABA increased the relative water content (RWC) of drought-stressed leaves via, reducing water loss, but MT increased it via, promoting water uptake efficiency; (2) for enzymatic antioxidant system, ABA and MT might modulate different kinds of superoxide dismutase to catalyze the reduction of O 2 À under drought; and (3) for ascorbic acid (AsA)-glutathione (GSH) cycle, MT increased the glutathione reductase activity in drought-stressed leaves, but ABA did not. ABA + MT spraying led to higher leaf RWC and total antioxidant capacity than single hormone under drought, leading to a lower H 2 O 2 level. For the enzymatic antioxidant system, single hormone treatment affected Cu/ZnSOD or MnSOD expression, but ABA + MT upregulated both genes in drought-stressed leaves. Hormones combined application also had higher CAT expression than single hormone. For AsA-GSH cycle, ABA + MT had higher dehydroascorbic acid reductase activity than single hormone, resulting in higher AsA content. Moreover, hormones combined application caused higher ascorbate peroxidase activity than single hormone, suggesting that their combination synergistically improved the ability of AsA to eliminate ROS. All these confirmed that ABA plus MT had synergistic effects on improving crop drought resistance.

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