By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive...

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.

With the increasing complexity of network attacks, an active defense based on intelligence sharing becomes crucial. There is an important issue in intelligence analysis that automatically extracts threat actions from cyber threat intelligence (CTI) reports. To address this problem, we propose EX-Action, a framework for extracting threat actions from CTI reports. EX-Action finds threat actions by employing the natural language processing (NLP) technology and identifies actions by a multimodal learning algorithm. At the same time, a metric is used to evaluate the information completeness of the extracted action obtained by EX-Action. By the experiment on the CTI reports that consisted of sentences with complex structure, the experimental result indicates that EX-Action can achieve better performance than two state-of-the-art action extraction methods in terms of accuracy, recall, precision, and F1-score.

In data‐driven big data security analysis, knowledge graph‐based multisource heterogeneous threat data organization, association mining, and inference analysis attach increasinginterest in the field of cybersecurity. Although the construction of knowledge graph based on deep learning has achieved great success, the construction of a largescale, high‐quality, and domain‐specific knowledge graph needs a manual annotation of large corpora, which means it is very difficult. To tackle this problem, we present a straightforward active learning strategy for cybersecurity entity recognition utilizing deep learning technology. BERT pre‐trained model and residual dilation convolutional neural networks (RDCNN) are introduced to learn entity context features, and the conditional random field (CRF) layer is employed as a tag decoder. Then, taking advantages of the output results and distribution of cybersecurity entities, we propose an active learning strategy named TPCL that considers the uncertainty, confidence, and diversity. We evaluated TPCL on the general domain datasets and cybersecurity datasets, respectively. The experimental results show that TPCL performs better than the traditional strategies in terms of accuracy and F1. Moreover, compared with the general field, it has better performance in the cybersecurity field and is more suitable for the Chinese entity recognition task in this field.