Cyber-Resilient Network Security Architecture For Al-Haramain Digital Infrastructure: Threat Detection, Zero Trust And Operational Continuity At The Grand Mosque In Makkah

References

[1] E. A. Felemban, F. U. Rehman, S. A. A. Biabani, A. Ahmad, A. Naseer, A. R. M. Abdul Majid, O. K. Hussain, A. M. Qamar, R. Falemban, and F. Zanjir, “Digital Revolution for Hajj Crowd Management: A Technology Survey,” IEEE Access, vol. 8, pp. 208583–208609, 2020, doi: 10.1109/ACCESS.2020.3037396. Crossref

[2] W. Albattah, M. H. K. Khel, S. Habib, M. Islam, S. Khan, and K. A. Kadir, “Hajj Crowd Management Using CNN-Based Approach,” Computers, Materials & Continua, vol. 66, no. 2, pp. 2183–2197, 2021, doi: 10.32604/cmc.2020.014227. Tech Science Press

[3] A. A. A. Abalkhail and S. M. A. Al Amri, “Saudi Arabia's Management of the Hajj Season through Artificial Intelligence and Sustainability,” Sustainability, vol. 14, no. 21, Art. no. 14142, 2022, doi: 10.3390/su142114142. MDPI

[4] F. Gazzawe and M. Albahar, “Reducing traffic congestion in Makkah during Hajj through the use of AI technology,” Heliyon, vol. 10, no. 1, Art. no. e23304, 2024, doi: 10.1016/j.heliyon.2023.e23304. ScienceDirect

[5] Saudi Press Agency, “Grand Mosque Gets Improved Connectivity, AI Applications through High-Tech Upgrade,” Apr. 2, 2024. Saudi Press Agency

[6] Saudi Press Agency, “Two Holy Mosques General Authority Uses AI at Grand Mosque Gates,” Jun. 11, 2025. Saudi Press Agency

[7] Z. Fan, H. Zhang, Z. Zhang, G. Lu, Y. Zhang, and Y. Wang, “A survey of crowd counting and density estimation based on convolutional neural network,” Neurocomputing, vol. 472, pp. 224–251, 2022, doi: 10.1016/j.neucom.2021.02.103. ScienceDirect

[8] L. Deng, Q. Zhou, S. Wang, J. M. Górriz, and Y. Zhang, “Deep learning in crowd counting: A survey,” CAAI Transactions on Intelligence Technology, vol. 9, no. 5, pp. 1043–1077, 2024, doi: 10.1049/cit2.12241. Wiley

[9] L. Yuan, Y. Chen, H. Wu, W. Wan, and P. Chen, “Crowd counting via Localization Guided Transformer,” Computers & Electrical Engineering, vol. 104, Art. no. 108430, 2022, doi: 10.1016/j.compeleceng.2022.108430. ScienceDirect

[10] Z. Miao, Y. Zhang, Y. Peng, H. Peng, and B. Yin, “DTCC: Multi-level dilated convolution with transformer for weakly-supervised crowd counting,” Computational Visual Media, vol. 9, pp. 859–873, 2023, doi: 10.1007/s41095-022-0313-5. Springer

[11] H. Gao, W. Zhao, D. Zhang, and M. Deng, “Application of improved transformer based on weakly supervised in crowd localization and crowd counting,” Scientific Reports, vol. 13, Art. no. 1144, 2023, doi: 10.1038/s41598-022-27299-0. Nature

[12] J. Yi, F. Chen, Z. Shen, Y. Xiang, S. Xiao, and W. Zhou, “An Effective Lightweight Crowd Counting Method Based on an Encoder-Decoder Network for Internet of Video Things,” IEEE Internet of Things Journal, vol. 11, no. 2, pp. 3082–3094, 2024, doi: 10.1109/JIOT.2023.3294727. IEEE

[13] C. Lin and X. Hu, “Efficient crowd density estimation with edge intelligence via structural reparameterization and knowledge transfer,” Applied Soft Computing, vol. 154, Art. no. 111366, 2024, doi: 10.1016/j.asoc.2024.111366. ScienceDirect

[14] R. Ahmad and I. Alsmadi, “Machine learning approaches to IoT security: A systematic literature review,” Internet of Things, vol. 14, Art. no. 100365, 2021, doi: 10.1016/j.iot.2021.100365. ScienceDirect

[15] A. Khraisat and A. Alazab, “A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges,” Cybersecurity, vol. 4, Art. no. 18, 2021, doi: 10.1186/s42400-021-00077-7. Springer

[16] S. Dina and D. Manivannan, “Intrusion detection based on Machine Learning techniques in computer networks,” Internet of Things, vol. 16, Art. no. 100462, 2021, doi: 10.1016/j.iot.2021.100462. Crossref

[17] M. A. Alsoufi, S. Razak, M. M. Siraj, I. Nafea, F. A. Ghaleb, F. Saeed, and M. Nasser, “Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review,” Applied Sciences, vol. 11, no. 18, Art. no. 8383, 2021, doi: 10.3390/app11188383. MDPI

[18] P. Ranaweera, A. D. Jurcut, and M. Liyanage, “Survey on Multi-Access Edge Computing Security and Privacy,” IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 1078–1124, 2021, doi: 10.1109/COMST.2021.3062546. IEEE

[19] V. Mothukuri, P. Khare, R. M. Parizi, A. Pouriyeh, A. Dehghantanha, and G. Srivastava, “Federated-Learning-Based Anomaly Detection for IoT Security Attacks,” IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2545–2554, 2022, doi: 10.1109/JIOT.2021.3077803. IEEE

[20] E. Marmol Campos, P. Fernandez Saura, A. Gonzalez-Vidal, J. L. Hernandez-Ramos, J. Bernal Bernabe, G. Baldini, and A. Skarmeta, “Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges,” Computer Networks, vol. 203, Art. no. 108661, 2022, doi: 10.1016/j.comnet.2021.108661. Crossref

[21] S. Agrawal, S. Sarkar, O. Aouedi, G. Yenduri, K. Piamrat, M. Alazab, S. Bhattacharya, P. K. R. Maddikunta, and T. R. Gadekallu, “Federated Learning for intrusion detection system: Concepts, challenges and future directions,” Computer Communications, vol. 195, pp. 346–361, 2022, doi: 10.1016/j.comcom.2022.09.012. Crossref

[22] M. Sarhan, W. W. Lo, S. Layeghy, and M. Portmann, “HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection,” Computers & Electrical Engineering, vol. 103, Art. no. 108379, 2022, doi: 10.1016/j.compeleceng.2022.108379. Crossref

[23] Z. Zhang, Y. Zhang, D. Guo, L. Yao, and Z. Li, “SecFedNIDS: Robust defense for poisoning attack against federated learning-based network intrusion detection system,” Future Generation Computer Systems, vol. 134, pp. 154–169, 2022, doi: 10.1016/j.future.2022.04.010. Crossref

[24] S. Li, M. Iqbal, and N. Saxena, “Future Industry Internet of Things with Zero-trust Security,” Information Systems Frontiers, vol. 26, pp. 1653–1666, 2024, doi: 10.1007/s10796-021-10199-5. Springer

[25] Zanasi, S. Russo, and M. Colajanni, “Flexible zero trust architecture for the cybersecurity of industrial IoT infrastructures,” Ad Hoc Networks, vol. 156, Art. no. 103414, 2024, doi: 10.1016/j.adhoc.2024.103414. Crossref

[26] S. Ameer, L. Praharaj, R. Sandhu, S. Bhatt, and M. Gupta, “ZTA-IoT: A Novel Architecture for Zero-Trust in IoT Systems and an Ensuing Usage Control Model,” ACM Transactions on Privacy and Security, vol. 27, no. 3, Art. no. 22, pp. 1–36, 2024, doi: 10.1145/3671147. ACM

[27] G. Epiphaniou, M. Hammoudeh, H. Yuan, C. Maple, and U. Ani, “Digital twins in cyber effects modelling of IoT/CPS points of low resilience,” Simulation Modelling Practice and Theory, vol. 125, Art. no. 102744, 2023, doi: 10.1016/j.simpat.2023.102744. Crossref

[28] T. N. I. Alrumaih, M. J. F. Alenazi, N. A. AlSowaygh, A. A. Humayed, and I. A. AlAblani, “Cyber resilience in industrial networks: A state of the art, challenges, and future directions,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 9, Art. no. 101781, 2023, doi: 10.1016/j.jksuci.2023.101781. ScienceDirect

[29] General Authority for the Care and Management of the Grand Mosque and the Prophet’s Mosque, “The General Authority for the Care and Management of the Grand Mosque and the Prophet’s Mosque Adopts Smart Technologies for Visitor Counting and Enhancing Crowd Management at the Grand Mosque,” Mar. 1, 2026. General Authority for the Care and Management of the Grand Mosque and the Prophet’s Mosque

[30] Saudi Press Agency, “Smart Systems Streamline Crowds at Grand Mosque,” Arab News, Jan. 14, 2026. Arab News

@article{1723365,
      author = {Ilyas Siddiqui Mohammad},
      title = {Cyber-Resilient Network Security Architecture For Al-Haramain Digital Infrastructure: Threat Detection, Zero Trust And Operational Continuity At The Grand Mosque In Makkah},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2834-2847},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723365.pdf},
      abstract = {The Al-Haramain operational environment increasingly depends on interconnected sensors, smart cameras, edge-computing nodes, high-capacity networks, identity services, analytics platforms and control-room applications. This dependency expands the cyber-attack surface of a safety-critical digital infrastructure in which loss of availability, manipulation of data, credential compromise or delayed detection could disrupt operational awareness. This review therefore examines cybersecurity and network resilience as its primary subject; real-time crowd monitoring is used only as the operational case through which security requirements are evaluated. An integrative review of literature published from 2020 to 2025 synthesises evidence on Internet of Things security, network intrusion detection, multi-access edge-computing security, federated anomaly detection, zero-trust architecture, microsegmentation, model integrity and cyber recovery. The study identifies principal attack paths across field devices, edge workloads, 5G and fibre transport, application services, data and machine-learning pipelines, privileged identities and third-party dependencies. It proposes a literature-derived conceptual/reference defence-in-depth architecture comprising cryptographic device identity, secure boot and attestation, default-deny segmentation, encrypted communications, continuous network and workload telemetry, adaptive threat detection, least-privilege access, protected model pipelines, immutable logging, verified backups and orchestrated recovery. Cyber resilience is assessed through mean time to detect and contain, attack-path reduction, trusted-service coverage, failover time, recovery integrity and bounded degradation. The architecture is not presented as an operationally validated cybersecurity framework; a representative cyber-range/simulation pathway is specified for future empirical testing. The review concludes that Al-Haramain digital services require an identity-centred, observable and recoverable security architecture capable of maintaining trusted minimum operations during partial compromise.},
      month = {September},
  }