Information Embedding for Secure Manufacturing: Challenges and Research Opportunities

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computing and Information Science in Engineering Pub Date : 2023-05-23 DOI:10.1115/1.4062600
K. Elsayed, Adam Dachowicz, M. Atallah, Jitesh H. Panchal
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引用次数: 1

Abstract

The digitization of manufacturing has transformed the product realization process across many industries, from aerospace and automotive to medicine and healthcare. While this progress has accelerated product development cycles and enabled designers to create products with previously unachievable complexity and precision, it has also opened the door to a broad array of unique security concerns, from theft of intellectual property to supply chain attacks and counterfeiting. To address these concerns, information embedding (e.g., watermarks and fingerprints) has emerged as a promising solution that enhances product security and traceability. Information embedding techniques involve storing unique and secure information within parts, making these parts easier to track and to verify for authenticity. However, a successful information embedding scheme requires information to be transmitted in physical parts both securely and in a way that is accessible to end users. Ensuring these qualities introduces unique computational and engineering challenges. For instance, these qualities require the embedding scheme designer to have an accurate model of the cyber-physical processes needed to embed information during manufacturing and read that information later in the product life cycle, as well as models of the cyber-physical, economic, and/or industrial processes that may degrade that information through natural wear-and-tear, or through intentional attacks by determined adversaries. This paper discusses challenges and research opportunities for the engineering design and manufacturing community in developing methods for efficient information embedding in manufactured products.
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面向安全制造的信息嵌入:挑战与研究机遇
制造业的数字化已经改变了许多行业的产品实现过程,从航空航天和汽车到医药和医疗保健。虽然这一进步加快了产品开发周期,使设计师能够创造出以前无法实现的复杂性和精度的产品,但它也为一系列独特的安全问题打开了大门,从知识产权盗窃到供应链攻击和假冒。为了解决这些问题,信息嵌入(例如,水印和指纹)已经成为一种有前途的解决方案,可以提高产品的安全性和可追溯性。信息嵌入技术涉及在零件中存储唯一和安全的信息,使这些零件更容易跟踪和验证真实性。然而,一个成功的信息嵌入方案要求信息在物理部件中以安全的方式传输,并以最终用户可访问的方式传输。确保这些品质引入了独特的计算和工程挑战。例如,这些品质要求嵌入方案设计者拥有在制造过程中嵌入信息所需的网络物理过程的精确模型,以及在产品生命周期后期读取该信息所需的网络物理、经济和/或工业过程的模型,这些模型可能会通过自然磨损或通过有决心的对手的故意攻击而降低该信息。本文讨论了工程设计和制造界在开发制造产品中有效信息嵌入方法方面面临的挑战和研究机遇。
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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