Building Cyber-Resilient Industrial IOT Ecosystems in Saudi Arabia: A Risk-Based Security Framework for Smart Manufacturing Under Vision 2030

References

[1] T. Gebremichael, L. P. I. Ledwaba, M. H. Eldefrawy, G. P. Hancke, N. Pereira, M. Gidlund, and J. Akerberg, “Security and Privacy in the Industrial Internet of Things: Current Standards and Future Challenges,” IEEE Access, vol. 8, pp. 152351–152366, 2020, doi: 10.1109/ACCESS.2020.3016937. Crossref

[2] K. P. Tange, M. De Donno, X. Fafoutis, and N. Dragoni, “A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2489–2520, 2020, doi: 10.1109/COMST.2020.3011208. Crossref

[3] J. Sengupta, S. Ruj, and S. Das Bit, “A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT,” Journal of Network and Computer Applications, vol. 149, Art. no. 102481, 2020, doi: 10.1016/j.jnca.2019.102481. Crossref

[4] P. Radanliev, D. De Roure, K. Page, J. R. C. Nurse, R. M. Montalvo, O. Santos, L. Maddox, and P. Burnap, “Cyber risk at the edge: Current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains,” Cybersecurity, vol. 3, Art. no. 13, 2020, doi: 10.1186/s42400-020-00052-8. Crossref

[5] I. Mugarza, J. L. Flores, and J. L. Montero, “Security Issues and Software Updates Management in the Industrial Internet of Things (IIoT) Era,” Sensors, vol. 20, no. 24, Art. no. 7160, 2020, doi: 10.3390/s20247160. MDPI

[6] V. Mullet, P. Sondi, and E. Ramat, “A Review of Cybersecurity Guidelines for Manufacturing Factories in Industry 4.0,” IEEE Access, vol. 9, pp. 23235–23263, 2021, doi: 10.1109/ACCESS.2021.3056650. Crossref

[7] S. F. Tan and A. Samsudin, “Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey,” Sensors, vol. 21, no. 19, Art. no. 6647, 2021, doi: 10.3390/s21196647. MDPI

[8] A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and A. Anwar, “TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems,” IEEE Access, vol. 8, pp. 165130–165150, 2020, doi: 10.1109/ACCESS.2020.3022862. Crossref

[9] M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, “Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning,” IEEE Access, vol. 10, pp. 40281–40306, 2022, doi: 10.1109/ACCESS.2022.3165809. Crossref

[10] M. Al-Hawawreh, E. Sitnikova, and N. Aboutorab, “X-IIoTID: A Connectivity-Agnostic and Device-Agnostic Intrusion Data Set for Industrial Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3962–3977, 2022, doi: 10.1109/JIOT.2021.3102056. Crossref

[11] R. V. Mendonca, J. C. Silva, R. L. Rosa, M. Saadi, D. Z. Rodriguez, and A. Farouk, “A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms,” Expert Systems, vol. 39, no. 5, Art. no. e12917, 2022, doi: 10.1111/exsy.12917. Wiley

[12] B. Alotaibi, “A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities,” Sensors, vol. 23, no. 17, Art. no. 7470, 2023, doi: 10.3390/s23177470. MDPI

[13] S. Soliman, W. Oudah, and A. Aljuhani, “Deep learning-based intrusion detection approach for securing industrial Internet of Things,” Alexandria Engineering Journal, vol. 81, pp. 371–383, 2023, doi: 10.1016/j.aej.2023.09.023. Crossref

[14] D. Zhang, Q.-G. Wang, G. Feng, Y. Shi, and A. V. Vasilakos, “A survey on attack detection, estimation and control of industrial cyber-physical systems,” ISA Transactions, vol. 116, pp. 1–16, 2021, doi: 10.1016/j.isatra.2021.01.036. Crossref

[15] S. Teerakanok, T. Uehara, and A. Inomata, “Migrating to Zero Trust Architecture: Reviews and Challenges,” Security and Communication Networks, vol. 2021, Art. no. 9947347, 2021, doi: 10.1155/2021/9947347. Crossref

[16] N. F. Syed, S. W. Shah, A. Shaghaghi, A. Anwar, Z. Baig, and R. Doss, “Zero Trust Architecture (ZTA): A Comprehensive Survey,” IEEE Access, vol. 10, pp. 57143–57179, 2022, doi: 10.1109/ACCESS.2022.3174679. Crossref

[17] A. Makkar, T. W. Kim, A. K. Singh, J. Kang, and J. H. Park, “SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT from Data Breach,” IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6406–6414, 2022, doi: 10.1109/TII.2022.3149902. Crossref

[18] S. M. S. Bukhari, M. H. Zafar, M. Abou Houran, Z. Qadir, S. K. R. Moosavi, and F. Sanfilippo, “Enhancing cybersecurity in Edge IIoT networks: An asynchronous federated learning approach with a deep hybrid detection model,” Internet of Things, vol. 27, Art. no. 101252, 2024, doi: 10.1016/j.iot.2024.101252. Crossref

[19] A. Annarelli, F. Nonino, and G. Palombi, “Understanding the management of cyber resilient systems,” Computers & Industrial Engineering, vol. 149, Art. no. 106829, 2020, doi: 10.1016/j.cie.2020.106829. Crossref

[20] J. Loonam, J. Zwiegelaar, V. Kumar, and C. Booth, “Cyber-Resiliency for Digital Enterprises: A Strategic Leadership Perspective,” IEEE Transactions on Engineering Management, vol. 69, no. 6, pp. 3757–3770, 2022, doi: 10.1109/TEM.2020.2996175. Crossref

[21] A. Kott and I. Linkov, “To Improve Cyber Resilience, Measure It,” Computer, vol. 54, no. 2, pp. 80–85, 2021, doi: 10.1109/MC.2020.3038411. Crossref

[22] M. Toussaint, S. Krima, and H. Panetto, “Industry 4.0 data security: A cybersecurity frameworks review,” Journal of Industrial Information Integration, vol. 39, Art. no. 100604, 2024, doi: 10.1016/j.jii.2024.100604. Crossref

[23] V. R. Kebande and A. I. Awad, “Industrial Internet of Things Ecosystems Security and Digital Forensics: Achievements, Open Challenges, and Future Directions,” ACM Computing Surveys, vol. 56, no. 5, Art. no. 131, pp. 1–37, 2024, doi: 10.1145/3635030. ACM

[24] D. Attique, W. Hao, W. Ping, D. Javeed, and P. Kumar, “Explainable and Data-Efficient Deep Learning for Enhanced Attack Detection in IIoT Ecosystem,” IEEE Internet of Things Journal, vol. 11, no. 24, pp. 38976–38986, 2024, doi: 10.1109/JIOT.2024.3384374. IEEE

[25] N. Z. Jhanjhi, M. Humayun, and S. N. Almuayqil, “Cyber Security and Privacy Issues in Industrial Internet of Things,” Computer Systems Science and Engineering, vol. 37, no. 3, pp. 361–380, 2021, doi: 10.32604/csse.2021.015206. Tech Science Press

[26] T. Alatawi and A. Aljuhani, “Anomaly Detection Framework in Fog-to-Things Communication for Industrial Internet of Things,” Computers, Materials & Continua, vol. 73, no. 1, pp. 1067–1086, 2022, doi: 10.32604/cmc.2022.029283. Crossref

[27] A. A. Aljuaid, S. A. Masood, and J. A. Tipu, “Integrating Industry 4.0 for Sustainable Localized Manufacturing to Support Saudi Vision 2030: An Assessment of the Saudi Arabian Automotive Industry Model,” Sustainability, vol. 16, no. 12, Art. no. 5096, 2024, doi: 10.3390/su16125096. MDPI

[28] A. Alablani and M. J. F. Alenazi, “Robust Attack Detection Framework Using Pretrained CNN Model for the Edge Industrial IoT Networks,” Concurrency and Computation: Practice and Experience, vol. 37, Art. no. e70206, 2025, doi: 10.1002/cpe.70206. Wiley

[29] I. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey,” Security and Communication Networks, vol. 2021, Art. no. 7142048, 2021.

[30] S. Almarri and A. Aljughaiman, “Blockchain Technology for IoT Security and Trust: A Comprehensive Systematic Literature Review,” Sustainability, vol. 16, no. 23, Art. no. 10177, 2024, doi: 10.3390/su162310177. MDPI

[31] Saudi Vision 2030, “Vision 2030 and Vision Realization Programs,” Official Government Portal. Accessed: Sep. 17, 2026. Saudi Vision 2030

[32] Saudi Vision 2030, National Industrial Development and Logistics Program Delivery Plan 2021–2025. Kingdom of Saudi Arabia, 2021. Saudi Vision 2030

[33] National Cybersecurity Authority, Essential Cybersecurity Controls (ECC 2-2024). Kingdom of Saudi Arabia, 2024. NCA

[34] National Cybersecurity Authority, Operational Technology Cybersecurity Controls (OTCC-1:2022). Kingdom of Saudi Arabia, 2022. NCA

[35] National Institute of Standards and Technology, Guide to Operational Technology (OT) Security, NIST Special Publication 800-82 Rev. 3. Gaithersburg, MD, USA: National Institute of Standards and Technology, 2023, doi: 10.6028/NIST.SP.800-82r3. NIST

[36] International Electrotechnical Commission, “IEC 62443 Series: Security for Industrial Automation and Control Systems.” IEC

@article{1723366,
      author = {Ishaq Siddiqui Mohammed},
      title = {Building Cyber-Resilient Industrial IOT Ecosystems in Saudi Arabia: A Risk-Based Security Framework for Smart Manufacturing Under Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2848-2870},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723366.pdf},
      abstract = {Industrial Internet of Things (IIoT) adoption is central to smart manufacturing because connected sensors, controllers, edge platforms, cloud services, and analytics can improve visibility, flexibility, and productivity. The same integration creates cyber-physical dependencies in which compromise of identity, firmware, communications, data, or control logic can propagate into downtime, unsafe states, quality loss, and supply-chain disruption. This structured integrative review synthesizes 30 peer-reviewed, DOI-verified studies published from 2020 to 2025 and separates that scholarly corpus from a second contextual layer of authoritative Saudi policy and cybersecurity documents. with inclusion limited to studies directly addressing IIoT threats, industrial detection and datasets, trust architecture, lifecycle security, cyber-resilience management, forensics, or Saudi smart-manufacturing context. Because the original record-export log was unavailable, the review does not claim PRISMA-complete record-flow counts or exhaustive coverage. The synthesis indicates that isolated preventive controls are insufficient for heterogeneous industrial environments with long-lived OT, constrained devices, remote support, and expanding cloud-edge connectivity. An evidence-informed six-dimension conceptual framework is therefore proposed, linking governance, asset and identity visibility, lifecycle assurance, monitoring, cyber-physical response and recovery, and measurable learning to operational consequences and ownership. The framework has been examined for contextual coherence across greenfield, brownfield, and multi-site scenarios but has not yet been empirically validated in Saudi manufacturing; validation through structured expert assessment, case studies, exercises, and longitudinal operational metrics is required.},
      keywords = {Industrial Internet of Things (IIoT); cyber resilience; smart manufacturing; Saudi Arabia; Vision 2030; risk-based security; Industry 4.0; operational technology},
      month = {September},
  }