Protecting Digital Twin Networks for 6G-enabled Industry 5.0 Ecosystems
Abstract
New industrial paradigms, such as the Industrial Internet of Things (IIoT) and Industry 5.0, are emerging in industrial contexts with the aim of fostering quality in operational processes. With the expected launch of 6G in the coming years, IIoT networks in Industry 5.0 ecosystems can leverage 6G technology and its support for training machine learning models using Digital Twins (DTs), embedded in DT Networks (DTNs), to transparently and continuously optimize their communications. Unfortunately, the use of these technologies, in turn, intensifies the attack surface and poses a serious threat to the new goals of Industry 5.0, such as improving the user experience, sustainability and resilience. This article therefore proposes a layered protection framework for 6G-enabled IIoT environments, where not only DTs and DTNs are fully protected, but also the whole 6G ecosystem, complying with the expected goals of Industry 5.0. To achieve this, the framework identifies for each protection layer a set of security and privacy services to subsequently relate them to existing computing infrastructures (cloud, edge, edge-cloud) and provide the best approach for future IIoT deployments.
References
[1]
Z. Yin et al., “Cybertwin-Enabled 6G Space-Air-Ground Integrated Networks: Architecture, Open Issue, and Challenges,” arXiv preprint arXiv:, 2022.
[2]
V.-L. Nguyen et al., “Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges,” IEEE Commun. Surveys & Tutorials, vol. 23, no. 4, 2021, pp. 2384–2428.
[3]
L. U. Khan et al., “Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities,” IEEE Commun. Surveys & Tutorials, vol. 24, no. 4, 4th Qtr. 2022, pp. 2230–54.
[4]
L. U. Khan et al., “Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions,” IEEE Commun. Mag., vol. 60, no. 1, 2022, pp. 74–80.
[5]
C. Alcaraz and J. Lopez, “Digital Twin: A Comprehensive Survey of Security Threats,” IEEE Commun. Surveys & Tutorials, vol. 24, no. 3, 3rd Qtr. 2022, pp. 1475–1503.
[6]
S. Shahzadi et al., “6G Vision: Toward Future Collaborative Cognitive Communication (3C) Systems,” IEEE Commun. Standards Mag, vol. 5, no. 2, 2021, pp. 60–67.
[7]
W. Sun et al., “Adaptive Federated Learning and Digital Twin for Industrial Internet of Things,” IEEE Trans. Industrial lnformatics, vol. 17, no. 8, 2021, pp. 5605–14.
[8]
Y. Lu et al., “Adaptive Edge Association for Wireless Digital Twin Networks in 6G,” IEEE Internet of Things J., vol. 8, no. 22, 2021, pp. 16,219–30.
[9]
M. Breque, L. De Nul, and A. Petridis, “Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry,” European Commission, 2021; available: https://data.europa.eu/doi/10.2777/308407.
[10]
D. Je et al., “Toward 6G Security: Technology Trends, Threats, and Solutions,” IEEE Commun. Standards Mag., vol. 5, no. 3, 2021, pp. 64–71.
[11]
C. Zhou et al., “Digital Twin Network: Concepts and Reference Architecture,” Internet Research Task Force, IETF, draft version, 2022; available: https://datatracker.ietf.org/doc/draft-irtf-nmrg-network-digital-twin-arch/.
[12]
G. Shao, “Use Case Scenarios for Digital Twin Implementation Based on ISO 23247,” National Institute of Standards and Technology Advanced Manufacturing Series 400–2, 2021; available: https://nvlpubs.nist.gov/nistpubs/ams/NIST.AMSAOO-2.pdf.
[13]
ISO, “Automation Systems and Integration - A Digital Twin manufacturing framework - Part 2: Reference architecture,” ISO/DIS 23247–2:2020(E), ISO TC 184/SC 4/WG 15, 2020; available: https://www.iso.org/standard/78743.html.
[14]
C. Alcaraz and J. Lopez, “Wide-Area Situational Awareness for Critical Infrastructure Protection,” IEEE Computer, vol. 46, no. 4, 2013, pp. 30–37.
[15]
N. Kourtellis et al., “FLaaS: Federated Learning as a Service,” Proc. 1st Workshop on Distributed Machine Learning, New York, NY, USA: ACM, 2020, pp. 7–13.
Recommendations
Guest Editorial Data-Driven Innovation and Adversarial Learning Models for Industry 5.0 Toward Consumer Digital Ecosystems
In recent years, the integration of advanced technologies such as communication advancements (e.g., 5G), Artificial Intelligence (AI), industrial edge computing, and adversarial Machine Learning (ML) has accelerated the evolution of Industry 5.0 systems, ...
Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
Highlights- This paper proposes a theoretical framework for digital twin-based production optimization, which integrates industrial IoT data processing and machine ...
AbstractDigital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control ...
Ecosystems of Industry 4.0: Combining Technology and Human Power
MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystemsIoT, digital twins, co-bots, drones, Artificial Intelligence, clouds are the system components of Industry 4.0, this trend born to face hypercompetition. It aims in renewing processes using available technologies and impacts the whole industry ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2023.
Publisher
IEEE Press
Publication History
Published: 01 March 2023
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
Cited By
View allView Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in