Threat modelling for industrial cyber physical systems in the era of smart manufacturing

Jbair, Mohammad; Ahmad, Bilal; Maple, Carsten; Harrison, Robert

doi:10.1016/j.compind.2022.103611

Cited by 55 publications

(17 citation statements)

References 7 publications

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“…With quality management as an important component of the core competencies of manufacturing companies [27], manufacturing industries can effectively achieve performance improvement goals and competitive advantages by implementing quality management practices to meet the challenges of new global competition [28]. Emerging technologies such as industrial big data [29], the industrial Internet of Things (IIoT) [30], and cyberphysical systems [31] are driving the manufacturing industry toward a new era of smart manufacturing [32]. Smart manufacturing, represented by shared manufacturing and cloud manufacturing, is facing many challenges while helping to transform and upgrade the manufacturing industry.…”

Section: Advances In Quality Innovation and Quality Synergy Researchmentioning

confidence: 99%

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Zhang

Wang

et al. 2022

Processes

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Shared manufacturing provides a new path for the transformation and development of the manufacturing industry, but challenges such as low quality and poor positivity for quality improvement limit the positive role of shared manufacturing. Considering the influences of heterogeneous emotions of subjects on quality decision making, the theory of rank-dependent expected utility (RDEU) and evolutionary game theory were integrated to establish an evolutionary game model of shared manufacturing quality innovation synergy with multi-agent participation and analyze how sentiment affects motivation for quality improvement. The study showed that: (1) emotions, an irrational factor, can significantly change the stable state of the evolution of the shared manufacturing quality innovation synergetic system by influencing the decision-making behavior of decision makers; (2) in terms of the specific microscopic influence mechanism, rationality is the key to ensuring that the behavioral decisions of decision makers do not enshrine large systemic deviations. (3) In terms of the mechanism of heterogeneous emotions, when one party is optimistic, the deepening of the other party’s pessimism tends to bring positive effects; when one party is pessimistic, the deepening of the other party’s optimism tends to bring negative effects. The main management insights are as follows: (1) correctly recognizing and treating heterogeneous emotions of decision makers and regulating the formation and role of heterogeneous emotions of decision makers; (2) appropriately creating an atmosphere of pessimistic emotions, and guiding shared manufacturing to pay attention to manufacturing quality innovation synergy; (3) appropriately releasing favorable information about quality innovation synergy, and continuously promoting high-quality development of shared manufacturing. This study broadens the path of quality improvement in shared manufacturing and the scope of application of emotion theory in a certain sense.

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Section: Advances In Quality Innovation and Quality Synergy Researchmentioning

confidence: 99%

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Zhang

Wang

et al. 2022

Processes

View full text Add to dashboard Cite

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“…They also discuss the various IoT risk assessment techniques. To predict and evaluate cyber risks and safeguard industrial assets from future cyberattacks, the study [98] suggests a structured threat modelling technique for Industrial Cyber-Physical Systems (ICPS). The process includes categorizing ICPS assets according to their level of importance before examining the cyber security flaws, threats, risks, effects, and solutions.…”

Section: Overview Of Data Privacy Threat Modelling Techniques For Aut...mentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

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Intelligence-based applications have been increasingly deployed in every field of life including smart homes, smart cities, healthcare services, and autonomous systems where personal data is collected across heterogeneous sources and processed using "black-box" algorithms in opaque centralised servers. As a consequence, preserving the data privacy and security of these applications is of utmost importance. In this respect, a modelling technique for identifying potential data privacy threats and specifying countermeasures to mitigate the related vulnerabilities in such AI-based systems plays a significant role in preserving and securing personal data. Various threat modelling techniques have been proposed such as STRIDE, LINDDUN, and PASTA but none of them is sufficient to model the data privacy threats in autonomous systems. Furthermore, they are not designed to model compliance with data protection legislation like the EU/UK General Data Protection Regulation (GDPR), which is fundamental to protecting data owners' privacy as well as to preventing personal data from potential privacy-related attacks. In this article, we survey the existing threat modelling techniques for data privacy threats in autonomous systems and then analyse such techniques from the viewpoint of GDPR compliance. Following the analysis, We employ STRIDE and LINDDUN in autonomous cars, a specific use-case of autonomous systems, to scrutinise the challenges and gaps of the existing techniques when modelling data privacy threats. Prospective research directions for refining data privacy threats & GDPR-compliance modelling techniques for autonomous systems are also presented.

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“…i Asset: Asset is the most common term which is used in many security ontologies. Jbair et al [24] defines asset as" Industrial Cyber-Physical Systems (ICPS) components and services that threat actors aim to compromise". The Asset can represent configuration in IT and OT, software, hardware, or integrated subsystems, which can be impacted by vulnerabilities.…”

Section: Concepts In Secure Ontologymentioning

confidence: 99%

Industrial Internet of Things Security Modelling using Ontological Methods

Jarwar

FREng

Ani

et al. 2022

Proceedings of the 12th International Conference on the Internet of Things

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The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologies (ICT) has introduced newer security issues such as weak or lack of end-toend security. These challenges have weakened the interest of many critical infrastructure industries in adopting IIoT-enabled systems. Implementing security in IIoT is challenging because this involves many heterogeneous Information Technology (IT) and Operational Technology (OT) devices and complex interactions with humans, and the environments in which these are operated and monitored. This article presents the initial results of the PETRAS Secure Ontologies for Internet of Things Systems (SOfIoTS) project, which consists of key security concepts and a modular design of a base security ontology, which supports security knowledge representation and analysis of IIoT security.

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Threat modelling for industrial cyber physical systems in the era of smart manufacturing

Cited by 55 publications

References 7 publications

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Industrial Internet of Things Security Modelling using Ontological Methods

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