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 attention. It is d...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 attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.展开更多
随着大数据时代的到来,如何从多源异构数据中准确地识别网络安全实体是构建网络安全知识图谱的基础问题。因此本文针对网络安全相关文本数据,研究支持海量网络数据的安全实体识别算法,为构建网络安全知识图谱奠定基础。针对海量的文本...随着大数据时代的到来,如何从多源异构数据中准确地识别网络安全实体是构建网络安全知识图谱的基础问题。因此本文针对网络安全相关文本数据,研究支持海量网络数据的安全实体识别算法,为构建网络安全知识图谱奠定基础。针对海量的文本类网络数据中安全实体的高效精准抽取问题,本文基于Hadoop分布式计算框架提出改进的条件随机场(conditional random fields,CRF)算法,对数据集进行有效分割,实现安全实体的高效准确识别。在大规模真实网络数据集上的实验证明,本文提出的算法达到了较高的网络安全实体识别准确率,同时提高了识别的效率。展开更多
文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工...文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工业网络安全防护系统,提升了工业网络安全防护能力。展开更多
基金the National Natural Science Foundation of China (No. 61802081)the Guizhou Provincial Natural Science Foundation, China (No. 20161052)+2 种基金the Guizhou Provincial Public Big Data Key Laboratory Open Project, China (No. 2017BDKFJJ024)the Guizhou University Doctoral Fund, China (No. 201526)the Major Scientific and Technological Special Project of Guizhou Province, China (No. 20183001).
文摘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 attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.
文摘随着大数据时代的到来,如何从多源异构数据中准确地识别网络安全实体是构建网络安全知识图谱的基础问题。因此本文针对网络安全相关文本数据,研究支持海量网络数据的安全实体识别算法,为构建网络安全知识图谱奠定基础。针对海量的文本类网络数据中安全实体的高效精准抽取问题,本文基于Hadoop分布式计算框架提出改进的条件随机场(conditional random fields,CRF)算法,对数据集进行有效分割,实现安全实体的高效准确识别。在大规模真实网络数据集上的实验证明,本文提出的算法达到了较高的网络安全实体识别准确率,同时提高了识别的效率。
文摘文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工业网络安全防护系统,提升了工业网络安全防护能力。
