Layered Security Guidance for Data Asset Management in Additive Manufacturing.

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computing and Information Science in Engineering Pub Date : 2024-01-01 DOI:10.1115/1.4064128
Fahad Ali Milaat, Joshua Lubell
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引用次数: 0

Abstract

Manufacturing industries are increasingly adopting additive manufacturing (AM) technologies to produce functional parts in critical systems. However, the inherent complexity of both AM designs and AM processes render them attractive targets for cyber-attacks. Risk-based Information Technology (IT) and Operational Technology (OT) security guidance standards are useful resources for AM security practitioners, but the guidelines they provide are insufficient without additional AM-specific revisions. Therefore, a structured layering approach is needed to efficiently integrate these revisions with preexisting IT and OT security guidance standards. To implement such an approach, this paper proposes leveraging the National Institute of Standards and Technology's Cybersecurity Framework (CSF) to develop layered, risk-based guidance for fulfilling specific security outcomes. It begins with an in-depth literature review that reveals the importance of AM data and asset management to risk-based security. Next, this paper adopts the CSF asset identification and management security outcomes as an example for providing AM-specific guidance and identifies the AM geometry and process definitions to aid manufacturers in mapping data flows and documenting processes. Finally, this paper uses the Open Security Controls Assessment Language to integrate the AM-specific guidance together with existing IT and OT security guidance in a rigorous and traceable manner. This paper's contribution is to show how a risk-based layered approach enables the authoring, publishing, and management of AM-specific security guidance that is currently lacking. The authors believe implementation of the layered approach would result in value-added, non-redundant security guidance for AM that is consistent with the preexisting guidance.

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增材制造数据资产管理分层安全指南》。
制造业越来越多地采用增材制造(AM)技术来生产关键系统中的功能部件。然而,AM 设计和 AM 工艺固有的复杂性使其成为网络攻击的目标。基于风险的信息技术 (IT) 和操作技术 (OT) 安全指导标准是 AM 安全从业人员的有用资源,但如果不针对 AM 进行额外的修订,这些标准所提供的指导是不够的。因此,需要一种结构化的分层方法,将这些修订与现有的 IT 和 OT 安全指导标准有效整合。为了实施这种方法,本文建议利用美国国家标准与技术研究院的网络安全框架(CSF)来制定分层的、基于风险的指南,以实现特定的安全成果。本文首先进行了深入的文献综述,揭示了 AM 数据和资产管理对基于风险的安全的重要性。接下来,本文以 CSF 资产识别和管理安全成果为例,提供 AM 专用指南,并确定 AM 几何形状和流程定义,以帮助制造商绘制数据流和记录流程。最后,本文使用开放式安全控制评估语言,以严格和可追溯的方式将 AM 专用指南与现有的 IT 和 OT 安全指南整合在一起。本文的贡献在于展示了基于风险的分层方法如何实现目前缺乏的 AM 专用安全指南的编写、发布和管理。作者认为,分层方法的实施将产生增值的、非冗余的 AM 安全指南,并与现有指南保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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