知识图谱在工业控制系统安全状况感知和决策中的应用研究:调查

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lixin Liu , Peihang Xu , Kefeng Fan , Mingyan Wang
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引用次数: 0

摘要

知识图谱作为知识组织和表示的有力工具,可以将分散的数据整合成统一的知识网络,实现知识关联和知识推理,从而提高安全态势感知的准确性。在工业控制系统安全决策中,知识图谱可以提供全面的知识支持,辅助决策者做出智能决策。本文综述了知识图谱在工业控制系统安全态势感知与决策领域的研究现状,包括数据驱动技术、基于规则的方法和基于知识图谱的方法。讨论了现有的知识图谱技术及其在工业控制系统安全态势感知和决策中的实际应用。最后,总结了知识图谱在工业控制系统安全态势感知与决策中应用所面临的一系列挑战,如数据分散、相关性和信息安全等,并对未来进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on application of knowledge graph in industrial control system security situation awareness and decision-making: A survey
Knowledge graph as a powerful tool for knowledge organization and representation, can integrate scattered data into a unified knowledge network, enabling knowledge association and knowledge reasoning, thus improving the accuracy of security situation awareness. In the context of industrial control system security decision-making, knowledge graphs can provide comprehensive knowledge support, assisting decision-makers in making intelligent decisions. This paper reviews the current research status of knowledge graphs in the field of industrial control system security situation awareness and decision-making, covering data-driven techniques, rule-based methods, and knowledge graph-based approaches. Existing knowledge graph technologies and their practical applications in industrial control system security situation awareness and decision-making are discussed. Finally, a series of challenges in the application of knowledge graphs in industrial control system security situation awareness and decision are summarized, such as data dispersion, correlation and information security, and the future prospects.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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