基于深度学习的威胁情报知识图谱构建技术
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摘要
随着网络威胁日益增多,威胁情报的知识图谱构建技术成为了网络安全领域的重要研究方向;然而,目前知识图谱构建技术对知识的获取缺乏快速性和准确性。针对这些问题,本文提出一种监督性的深度学习模型,对威胁情报的实体和实体关系进行自动化抽取,并通过图数据库进行知识图谱的可视化展示。实验结果表明,本文提出的基于深度学习模型对威胁情报实体和实体抽取的方法,在准确性上有着较大提高,为自动化构建威胁情报知识图谱提供有力的保障。
With the increasing number of cyber threats,the knowledge graph construction technology of threat intelligence has become an important research direction in the field of network security. However,the current knowledge graph construction technology lacks the speed and accuracy of knowledge acquisition. In view of these problems,this paper proposes a supervised deep learning model,which automatically extracts the entity and entity relationship of threat intelligence,and visualizes the knowledge map through graph data. The experimental results show that the method based on the deep learning model for threat intelligence entities and entities extraction has a great improvement in accuracy,which provides a powerful guarantee for the automated construction of threat intelligence knowledge graph.
With the increasing number of cyber threats,the knowledge graph construction technology of threat intelligence has become an important research direction in the field of network security. However,the current knowledge graph construction technology lacks the speed and accuracy of knowledge acquisition. In view of these problems,this paper proposes a supervised deep learning model,which automatically extracts the entity and entity relationship of threat intelligence,and visualizes the knowledge map through graph data. The experimental results show that the method based on the deep learning model for threat intelligence entities and entities extraction has a great improvement in accuracy,which provides a powerful guarantee for the automated construction of threat intelligence knowledge graph.
引文
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[18]郭琳,翟社平,高山.知识图谱中的语义推理算法[J].计算机与现代化,2017(12):71-76.
[19]杨东,王移芝.基于Attention-based C-GRU神经网络的文本分类[J].计算机与现代化,2018(2):96-100.
[2]艾伦·利斯卡.防患未然:实施情报先导的信息安全方法与实践[M].姚军,吴冲华,译.北京:机械工业出版社,2016.
[3]葛斌,谭真,张翀,等.军事知识图谱构建技术[J].指挥与控制学报,2016,2(4):302-308.
[4] CHAPPER J R. Worldwide Threat Assessment of the U. S.Intelligence Community[Z]. Senate Armed Services Committee,2016.
[5] BARNUM S. Standardizing Cyber Threat Intelligence Information with the Structured Threat Information e Xpression(STIXTM)[Z]. Miter Corporation,2014.
[6]陈剑锋,范航博.面向网络空间安全的威胁情报本体化共享研究[J].通信技术,2018,51(1):171-177.
[7]袁旭萍.基于深度学习的商业领域知识图谱构建[D].上海:华东师范大学,2015.
[8]陶昱玮.网络威胁情报活动模型建构与解析[J].保密科学技术,2017(8):21-28.
[9]李超,周英.大数据环境下的威胁情报分析[J].情报杂志,2017,36(9):24-30.
[10]李建华.网络空间威胁情报感知、共享与分析技术综述[J].网络与信息安全学报,2016,2(2):16-18
[11]侯天祝.本体推理中组合推理机制的研究[D].成都:西南交通大学,2016.
[12]陈宇,郑德权,赵铁军.基于Deep Belief Nets的中文名实体关系抽取[J].软件学报,2012,23(10):2572-2585.
[13]黄炜.语义网技术在网络威胁情报分析系统的应用[D].北京:北方工业大学,2017.
[14]王凯琢.网络安全威胁态势评估与分析技术研究[D].哈尔滨:哈尔滨工程大学,2009.
[15]成鹏.基于语义Web的知识元集成模型研究[D].西安:西安电子科技大学,2007.
[16]陈毅波.基于关联数据和用户本体的个性化知识服务关键技术研究[D].武汉:武汉大学,2012.
[17]刘一阳.安全本体在社交网络中的应用研究[D].上海:上海交通大学,2011.
[18]郭琳,翟社平,高山.知识图谱中的语义推理算法[J].计算机与现代化,2017(12):71-76.
[19]杨东,王移芝.基于Attention-based C-GRU神经网络的文本分类[J].计算机与现代化,2018(2):96-100.