Search

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. [ABSTRACT FROM AUTHOR]

This research examines the association between the platelet-derived growth factor/platelet-derived growth factor receptor (PDGF/PDGFR) system and rheumatoid arthritis (RA) susceptibility through a comprehensive search of the PubMed database to study the expression of the PDGF/PDGFR system in RA. Review Manager software version 5.3 was used for statistical analysis. Six eligible studies published in the English language were included, including 108 rheumatoid arthritis cases and 85 controls with the corresponding 126 and 97 tests, respectively, relating the expression of the PDGF/PDGFR system to the risk of RA. The overall results indicated a significant association between the PDGF/PDGFR system expression and RA (OR = 5.25 , 95% CI: 3.00-9.18, p < 00001), RA patients in Asian countries (OR = 4.13 , 95% CI = 2.04 -8.39, p < 0.0001) and in Western countries (OR = 9.18 , 95% CI = 2.04 -8.39, p = 0.03), and only PDGF expression in RA patients (OR = 5.28 , 95% CI = 2.73 -10.21, p < 0.00001). Thus, only the PDGFR expression was insignificantly associated with RA susceptibility (OR = 9.25 , 95% CI = 0.63 -136.30, p = 0.11). Hence, the PDGF/PDGFR system most likely contributes to susceptibility to RA. [ABSTRACT FROM AUTHOR]