Digital Twin-Enabled Vulnerability Assessment with Zero Trust Policy Enforcement in Smart Manufacturing Cyber-Physical System

Authors

  • Chima Nwankwo Idika  Department of Information Technology, De Meek Builders Ltd, Umuahia, Abia State, Nigeria.
  • Ugoaghalam Uche James  Department of Computer Information Systems. College of Engineering, Prairie View A&M University, Praire View ,77446, Texas, USA.
  • Onuh Matthew Ijiga  Department of Physics, Joseph Sarwaan Tarkaa University, Makurdi, Benue State, Nigeria.
  • Lawrence Anebi Enyejo  Department of Telecommunications, Enforcement Ancillary and Maintenance, National Broadcasting Commission, Aso-Villa, Abuja, Nigeria

Keywords:

Digital Twin (DT), Zero Trust Architecture (ZTA), Cyber-Physical Systems (CPS), Smart Manufacturing, Vulnerability Assessment

Abstract

The convergence of digital twin (DT) technology and zero trust architecture (ZTA) offers a transformative framework for enhancing cybersecurity and operational resilience in smart manufacturing cyber-physical systems (CPS). This review explores how DTs—virtual representations of physical assets—can simulate, monitor, and evaluate vulnerabilities across complex manufacturing networks in real time. Traditional perimeter-based defenses are increasingly ineffective in distributed and interconnected industrial environments. In response, zero trust policy enforcement—anchored in the principles of "never trust, always verify"—introduces dynamic access controls, micro-segmentation, and continuous authentication that address latent security gaps in CPS. The integration of DTs with ZTA provides contextual awareness for asset behavior, enabling predictive threat modeling, anomaly detection, and proactive security orchestration. This paper reviews recent advancements in DT-enhanced vulnerability assessment tools, zero trust policy engines, and their interplay in manufacturing systems with high cyber-physical interdependence. Emphasis is placed on identifying research gaps, evaluating system architectures, and proposing future directions for implementing resilient, secure-by-design CPS infrastructures. By systematically reviewing case studies, industrial applications, and academic frameworks, this study underscores the critical role of DT and ZTA synergy in safeguarding smart manufacturing environments against evolving cyber threats.

References

  1. Abiodun, K., Ogbuonyalu, U. O., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2023). Exploring Cross-Border Digital Assets Flows and Central Bank Digital Currency Risks to Capital Markets Financial Stability. International Journal of Scientific Research and Modern Technology, 2(11), 32–45. https://doi.org/10.38124/ijsrmt.v2i11.447
  2. Alcaraz, C., & Lopez, J. (2022). Digital twin: A comprehensive survey of security threats. IEEE Communications Surveys & Tutorials, 24(3), 1475-1503.
  3. Alsmadi, I., & Xu, D. (2020). Security of cyber-physical systems using zero trust architecture. Computers & Security, 92, 101751. https://doi.org/10.1016/j.cose.2020.101751
  4. Alzubaidi, A., & Kalutarage, H. K. (2021). Policy-based access control for Zero Trust architecture in smart industrial systems. Computers & Security, 106, 102286. https://doi.org/10.1016/j.cose.2021.102286
  5. Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C., & Dijmărescu, I. (2021). Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability, 13(10), 5495.
  6. Atalor, S. I. (2019). Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy ICONIC RESEARCH AND ENGINEERING JOURNALS JUN 2019 | IRE Journals | Volume 2 Issue 12 | ISSN: 2456-8880
  7. Atalor, S. I., Ijiga, O. M., & Enyejo, J. O. (2023). Harnessing Quantum Molecular Simulation for Accelerated Cancer Drug Screening. International Journal of Scientific Research and Modern Technology, 2(1), 1–18. https://doi.org/10.38124/ijsrmt.v2i1.502
  8. Atalor, S. I., Raphael, F. O. & Enyejo, J. O. (2023). Wearable Biosensor Integration for Remote Chemotherapy Monitoring in Decentralized Cancer Care Models. International Journal of Scientific Research in Science and Technology Volume 10, Issue 3 (www.ijsrst.com) doi : https://doi.org/10.32628/IJSRST23113269
  9. Balta, E. C., Pease, M., Moyne, J., Barton, K., & Tilbury, D. M. (2023). Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems. IEEE Transactions on Automation Science and Engineering, 21(2), 1695-1712.
  10. Balta, E. C., Pease, M., Moyne, J., Barton, K., & Tilbury, D. M. (2023). Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems. IEEE Transactions on Automation Science and Engineering, 21(2), 1695-1712.
  11. Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
  12. Cunningham, C., & Pollard, J. (2017). The eight business and security benefits of zero trust. Forrester Reseach November.
  13. Federici, F., Martintoni, D., & Senni, V. (2023). A zero-trust architecture for remote access in industrial IoT infrastructures. Electronics, 12(3), 566.
  14. Fernández-Caramés, T. M., & Fraga-Lamas, P. (2020). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201–45218. https://doi.org/10.1109/ACCESS.2019.2908780
  15. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
  16. Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1–14. https://doi.org/10.2514/6.2012-1818
  17. Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
  18. He, Y., Xu, X., & Wang, L. (2021). Cybersecurity challenges for industrial cyber–physical systems: A comprehensive review. Robotics and Computer-Integrated Manufacturing, 68, 102114. https://doi.org/10.1016/j.rcim.2020.102114
  19. Ihimoyan, M. K., Enyejo, J. O. & Ali, E. O. (2022). Monetary Policy and Inflation Dynamics in Nigeria, Evaluating the Role of Interest Rates and Fiscal Coordination for Economic Stability. International Journal of Scientific Research in Science and Technology. Online ISSN: 2395-602X. Volume 9, Issue 6. doi : https://doi.org/10.32628/IJSRST2215454 
  20. Imoh, P. O. (2023). Impact of Gut Microbiota Modulation on Autism Related Behavioral Outcomes via Metabolomic and Microbiome-Targeted Therapies International Journal of Scientific Research and Modern Technology (IJSRMT) Volume 2, Issue 8, 2023 DOI: https://doi.org/10.38124/ijsrmt.v2i8.494
  21. Koulamas, C., & Kalogeras, A. P. (2018). Cyber-Physical Systems and Digital Twins in the Industrial Internet of Things: A Review. Computer Science Review, 30, 100–112. https://doi.org/10.1016/j.cosrev.2018.10.002
  22. Lee, J., Bagheri, B., & Jin, C. (2016). Cyber-physical systems for predictive production systems. CIRP Annals, 65(1), 715–728. https://doi.org/10.1016/j.cirp.2016.06.001
  23. Leng, J., Liu, Q., Ye, S., Jing, J., Wang, Y., & Zhang, H. (2021). Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of Manufacturing Systems, 58, 52–64. https://doi.org/10.1016/j.jmsy.2020.06.017
  24. Leng, J., Wang, D., Shen, W., Li, X., & Liu, Q. (2022). Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of Manufacturing Systems, 62, 731–749. https://doi.org/10.1016/j.jmsy.2022.01.007
  25. Li, X., Peng, J., Niu, J., Wu, F., Liao, J., & Choo, K. K. R. (2017). A robust and energy efficient authentication protocol for industrial internet of things. IEEE Internet of Things Journal, 5(3), 1606-1615.
  26. Li, Y., Tao, Z., Wang, L., Du, B., Guo, J., & Pang, S. (2023). Digital twin-based job shop anomaly detection and dynamic scheduling. Robotics and Computer-Integrated Manufacturing, 79, 102443.
  27. Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., ... & Chen, X. (2021). Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. Journal of Manufacturing Systems, 58, 52-64.
  28. McLaughlin, S., Konstantinou, C., Wang, X., Davi, L., Sadeghi, A. R., Maniatakos, M., & Karri, R. (2016). The cybersecurity landscape in industrial control systems. Proceedings of the IEEE, 104(5), 1039-1057.
  29. Mutahi, D. (2023). Navigating The Delicate Balance: Privacy and Data Security In Computing, https://www.linkedin.com/pulse/navigating-delicate-balance-privacy-data-security-computing-mutahi
  30. Ononiwu, M., Azonuche, T. I., & Enyejo, J. O. (2023). Exploring Influencer Marketing Among Women Entrepreneurs using Encrypted CRM Analytics and Adaptive Progressive Web App Development. International Journal of Scientific Research and Modern Technology, 2(6), 1–13. https://doi.org/10.38124/ijsrmt.v2i6.562
  31. Ononiwu, M., Azonuche, T. I., Imoh, P. O. & Enyejo, J. O. (2023). Exploring SAFe Framework Adoption for Autism-Centered Remote Engineering with Secure CI/CD and Containerized Microservices Deployment International Journal of Scientific Research in Science and Technology Volume 10, Issue 6 doi : https://doi.org/10.32628/IJSRST
  32. Ononiwu, M., Azonuche, T. I., Okoh, O. F., & Enyejo, J. O. (2023). AI-Driven Predictive Analytics for Customer Retention in E-Commerce Platforms using Real-Time Behavioral Tracking. International Journal of Scientific Research and Modern Technology, 2(8), 17–31. https://doi.org/10.38124/ijsrmt.v2i8.561
  33. Ononiwu, M., Azonuche, T. I., Okoh, O. F.. & Enyejo, J. O. (2023). Machine Learning Approaches for Fraud Detection and Risk Assessment in Mobile Banking Applications and Fintech Solutions International Journal of Scientific Research in Science, Engineering and Technology Volume 10, Issue 4 doi : https://doi.org/10.32628/IJSRSET
  34. Paul, B., & Rao, M. (2022). Zero-trust model for smart manufacturing industry. Applied Sciences, 13(1), 221.
  35. Paul, B., & Rao, M. (2022). Zero-trust model for smart manufacturing industry. Applied Sciences, 13(1), 221.
  36. Qi, Q., Tao, F., Zuo, Y., Zhao, D., & Lin, Y. (2021). Digital Twin Service Towards Smart Manufacturing. Journal of Manufacturing Systems, 58, 185–195. https://doi.org/10.1016/j.jmsy.2020.06.017
  37. Qi, Q., Zhao, D., Liao, T. W., & Tao, F. (2018). Modeling of cyber-physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing. In International manufacturing science and engineering conference (Vol. 51357, p. V001T05A018). American Society of Mechanical Engineers.
  38. Ray, P. P. (2023). Web3: A comprehensive review on background, technologies, applications, zero-trust architectures, challenges and future directions. Internet of Things and Cyber-Physical Systems, 3, 213-248.
  39. Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture. NIST Special Publication 800-207. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207
  40. Terca, K. (2022). Digital twin: Manufacturing case studies, smart factories and predictive maintenance, https://blog.3ds.com/brands/netvibes/digital-twin-manufacturing-case-studies-smart-factories-and-predictive-maintenance/
  41. Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., ... & Karkazis, P. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, 20(19), 5480.
  42. Vigna, R. (2023). The Tangible Benefits of Digital Twins: A New Era of Efficiency and Optimization https://www.linkedin.com/pulse/tangible-benefits-digital-twins-new-era-efficiency-rodrigo-vigna-x0zkc
  43. Wang, P., Xu, N., Zhang, H., Sun, W., & Benslimane, A. (2021). Dynamic access control and trust management for blockchain-empowered IoT. IEEE Internet of Things Journal, 9(15), 12997-13009.
  44. Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023). A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet of Things Journal, 10(17), 14965-1498
  45. Wen, J., Gabrys, B., & Musial, K. (2022). Toward digital twin oriented modeling of complex networked systems and their dynamics: A comprehensive survey. Ieee Access, 10, 66886-66923.
  46. Xie, J., Ma, H., Yu, L., & Fan, X. (2021). Real-time data-driven digital twin framework for complex equipment operation and maintenance in smart manufacturing. Advanced Engineering Informatics, 47, 101230. https://doi.org/10.1016/j.aei.2020.101230
  47. Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023). A survey on digital twin for industrial internet of things: Applications, technologies and tools. IEEE Communications Surveys & Tutorials, 25(4), 2569-2598.
  48. Xu, L. D., & Duan, L. (2020). Big Data for Cyber-Physical Systems in Industry 4.0: A Survey. Enterprise Information Systems, 14(2), 148–169. https://doi.org/10.1080/17517575.2019.1652321
  49. Yan, Z., Zhang, P., & Wang, Y. (2020). Dynamic trust management and adaptive access control for Zero Trust in smart industrial environments. Future Generation Computer Systems, 108, 1232–1244. https://doi.org/10.1016/j.future.2020.03.010
  50. Zheng, Y., Yang, S., Cheng, H., & Wang, T. (2021). Intelligent predictive maintenance strategy with digital twin for sustainable manufacturing systems. Journal of Cleaner Production, 316, 128321. https://doi.org/10.1016/j.jclepro.2021.128321

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-11-30

Issue

Volume 9, Issue 6, November-December-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chima Nwankwo Idika, Ugoaghalam Uche James, Onuh Matthew Ijiga, Lawrence Anebi Enyejo, " Digital Twin-Enabled Vulnerability Assessment with Zero Trust Policy Enforcement in Smart Manufacturing Cyber-Physical System" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.475-499, November-December-2023.