Defending against product-oriented cyber-physical attacks on machining systems

Shafae, Mohammed; Wells, Lee J.; Purdy, Gregory T.

doi:10.1007/s00170-019-03805-z

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…In [24], product-oriented attacks for machining processes are studied. In-situ monitoring of process variables including side-channel measurements is utilized for monitoring and attack detection.…”

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

“…In our framework, the measurement systems can be implemented as part of the physical process and/or via the Controller DT. The measurement data is processed by the Feature DT to evaluate the metrics mentioned in [24] (e.g., Material Removal Rate, cutting time, etc. ), logged in a database, and shared with the Cybersecurity DT.…”

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

“…), logged in a database, and shared with the Cybersecurity DT. The Cybersecurity DT may be implemented with the attack detection logic presented in [24], where the Consistency DT can implement STL logic to monitor the changes in these parameters to be within limits specified by an SME.…”

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

“…2. However, methods to differentiate anomalies in the context of attacks and dealing with advanced closed-loop controllers with set point changes and transient behavior has not been discussed in [24], [26].…”

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

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Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Balta

Pease

Moyne

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step towards implementing and securing highperformance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyberattacks. The second challenge is the identification of cyberattacks during transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyberattacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach.

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Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

Section: F Outlook For Additional Application Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Balta

Pease

Moyne

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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“…Shafae et al [61] propose a cyber-defence strategy that increases the difficulty/cost required for a successful attack on a manufacturing system. This strategy adapts quality control tools to act as physical detection layers to reach this goal-a machining-specific attack design scheme and an attack design designation system are proposed to provide the structure to populate a wide variety of potential attacks to manufacturing systems.…”

Section: Editorial Overview Of This Special Issue Papers and Their Comentioning

confidence: 99%

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

Cohen

Faccio

Pilati

et al. 2019

Int J Adv Manuf Technol

127

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The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

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Introducing randomness into univariate control chart sampling strategies for enhancing security against cyber‐physical attacks in production systems

Elhabashy

Wells

Camelio

2023

Quality & Reliability Eng

Self Cite

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With recent technological advancements, production systems have become more susceptible to cyber‐physical attacks. Such attacks can be intelligently designed to both alter product dimensions and avoid detection by current Quality Control (QC) tools. The objective of this work is to assess the performance of random sampling strategies for control charting in detecting cyber‐physical attacks. More specifically, the methodology adopts traditional random sampling approaches used for inspection and applies them to univariate control charts. Different random sampling strategies are discussed and their performances under varying attack scenarios are evaluated. In addition, new control chart performance metrics were developed for a more in‐depth analysis, to guide practitioners in assessing how well these control charts increase their robustness to cyber‐physical attacks.

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Defending against product-oriented cyber-physical attacks on machining systems

Cited by 9 publications

References 23 publications

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

Introducing randomness into univariate control chart sampling strategies for enhancing security against cyber‐physical attacks in production systems

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