工业 4.0 数据安全:网络安全框架审查

IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Marion Toussaint , Sylvère Krima , Hervé Panetto
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引用次数: 0

摘要

随着制造商采用数据驱动的决策和流程,制造业越来越容易受到数字威胁的影响,数据安全成为工业 4.0 的一大挑战。更具体地说,数据篡改被认为是对组织的严重威胁,会造成重大破坏性后果。采用网络安全框架对于保护组织免受这些网络威胁至关重要。这些框架往往是通用的、没有主见的,只能为降低网络风险提供高层次的指导。我们的目标是找到一个框架,它能为我们提供坚实的基础,我们可以对其进行定制,以支持我们应对数据操作风险的需求。本文旨在对网络安全框架进行审查,并找出最可定制但又最有主见的方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Industry 4.0 data security: A cybersecurity frameworks review

As manufacturers are adopting data-driven decisions and processes, manufacturing is becoming more vulnerable to digital threats, making data security a major challenge for Industry 4.0. More specifically, data manipulation is considered a serious threat to organizations with significant and damaging consequences. Adopting a cybersecurity framework is essential to protect organizations against these cyber threats. These frameworks are often generic, unopinionated, and only provide high-level guidance for mitigating cyber risk. Our objective is to find a framework that offers a solid foundation we can customize to support our needs for addressing data manipulation risk. This paper aims to review cybersecurity frameworks and identify the most customizable, yet opinionated, option.

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来源期刊
Journal of Industrial Information Integration
Journal of Industrial Information Integration Decision Sciences-Information Systems and Management
CiteScore
22.30
自引率
13.40%
发文量
100
期刊介绍: The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers. The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.
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