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Challenges and vulnerability assessment of cybersecurity in IoT-enabled SC

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Abstract

Integrating the Internet of Things (IoT) into urban infrastructure has reached critical mass as the movement toward Smart City (SC) development has gained momentum. Although the widespread use of IoT technology has led to more efficient and sustainable urban settings, the proliferation of linked devices and sensors has prompted legitimate safety concerns. Hence, cybersecurity has emerged as a significant obstacle to implementing SC technologies. The complexity of the SC environment necessitates additional security measures beyond the usual fare of firewalls and intrusion detection systems. To detect possible cybersecurity risks and vulnerabilities, this article presents a Challenge and Vulnerability Assessment Method (CVAT) for an IoT-enabled SC environment. The suggested method employs Internet of Things (IoT) sensors and gadgets to keep an eye on the SC’s ecosystem and spot any dangers that may arise. This research creates a simulation environment to show how well the suggested strategy works. The results of the sample tests demonstrate the effectiveness of the proposed CVAT in detecting security flaws and suggesting fixes. The proposed approach has several benefits over current processes, such as detecting real-time vulnerabilities, scaling, and adapting to various SC applications. Cybersecurity threats in IoT-enabled SC environments can be mitigated with the help of the proposed CVAT method.

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All data generated or analysed during this study are included in the manuscript.

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References

  1. Camero, A., & Alba, E. (2019). SC and information technology: A review. Cities, 93, 84–94.

    Article  Google Scholar 

  2. Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarci, B., & Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in SC. Sustainable Cities and Society, 50, 101660.

    Article  Google Scholar 

  3. Sharma, R., & Arya, R. (2022). Security threats and measures in the Internet of Things for SC infrastructure: A state of the art. Transactions on Emerging Telecommunications Technologies, e4571.

  4. Rani, S., Kataria, A., Chauhan, M., Rattan, P., Kumar, R., & Sivaraman, A. K. (2022). Security and privacy challenges in deploying cyber-physical systems in SC applications: State-of-art work. Materials Today: Proceedings, 62, 4671–4676.

    Google Scholar 

  5. Al-Turjman, F., Zahmatkesh, H., & Shahroze, R. (2022). An overview of security and privacy in SC’s IoT communications. Transactions on Emerging Telecommunications Technologies, 33(3), e3677.

    Article  Google Scholar 

  6. Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I. H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable SC. Sustainable Cities and Society, 63, 102364.

    Article  Google Scholar 

  7. Araujo, V., Mitra, K., Saguna, S., & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for SC. Journal of Parallel and Distributed Computing, 132, 250–261.

    Article  Google Scholar 

  8. Rahman, M. A., Asyhari, A. T., Leong, L. S., Satrya, G. B., Tao, M. H., & Zolkipli, M. F. (2020). Scalable machine learning-based intrusion detection system for IoT-enabled SC. Sustainable Cities and Society, 61, 102324.

    Article  Google Scholar 

  9. Andrade, R. O., Yoo, S. G., Tello-Oquendo, L., & Ortiz-Garcés, I. (2020). A comprehensive study of the IoT cybersecurity in SC. IEEE Access, 8, 228922–228941.

    Article  Google Scholar 

  10. Chen, D., Wawrzynski, P., & Lv, Z. (2021). Cyber security in SC: A review of deep learning-based applications and case studies. Sustainable Cities and Society, 66, 102655.

    Article  Google Scholar 

  11. Abd El-Latif, A. A., Abd-El-Atty, B., Mehmood, I., Muhammad, K., Venegas-Andraca, S. E., & Peng, J. (2021). Quantum-inspired blockchain-based cybersecurity: Securing smart edge utilities in IoT-based SC. Information Processing & Management, 58(4), 102549.

    Article  Google Scholar 

  12. Sengan, S., Subramaniyaswamy, V., Nair, S. K., Indragandhi, V., Manikandan, J., & Ravi, L. (2020). Enhancing cyber–physical systems with hybrid SC cyber security architecture for the secure public data-smart network. Future Generation Computer Systems, 112, 724–737.

    Article  Google Scholar 

  13. Alshambri, H., AlZain, M. A., Soh, B., Masud, M., & Al-Amri, J. (2020). Cybersecurity attacks on wireless sensor networks in SC: an exposition. International Journal of Scientific & Technology Research, 8(1).

  14. Doku, R., & Rawat, D. B. (2019). Big data in cybersecurity for SC applications. In: SC cybersecurity and Privacy (pp. 103–112). Elsevier.

  15. Sedik, A., Hammad, M., Abd El-Latif, A. A., El-Banby, G. M., Khalaf, A. A., Abd El-Samie, F. E., & Iliyasu, A. M. (2021). Deep learning modalities for biometric alteration detection in 5G networks-based secure SC. IEEE Access, 9, 94780–94788.

    Article  Google Scholar 

  16. Nguyen, H. P. D., & Nguyen, D. D. (2021). Drone application in SC: The general overview of security vulnerabilities and countermeasures for data communication. In: Development and future of the internet of drones (IoD): Insights, trends, and road ahead, pp. 185–210.

  17. Serrano, W. (2021). The blockchain random neural network for cybersecurity IoT and 5G infrastructure in SC. Journal of Network and Computer Applications, 175, 102909.

    Article  Google Scholar 

  18. Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. (2020). Cyberattacks detection in IoT-based SC applications using machine learning techniques. International Journal of environmental research and public health, 17(24), 9347.

    Article  Google Scholar 

  19. Choo, K. K. R., Gai, K., Chiaraviglio, L., & Yang, Q. (2021). A multidisciplinary approach to Internet of Things (IoT) cybersecurity and risk management. Computers & Security, 102, 102136.

    Article  Google Scholar 

  20. Elsaeidy, A. A., Jamalipour, A., & Munasinghe, K. S. (2021). A hybrid deep learning approach for replay and DDoS attack detection in an SC. IEEE Access, 9, 154864–154875.

    Article  Google Scholar 

  21. https://www.stratosphereips.org/datasets-iot23.

  22. https://inspector.engineering.nyu.edu.

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Funding

This research work was funded by Institutional Fund Projects under grant no. IFPIP: 236–611-1442. Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia.

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  1. Civil Engineering Department, University of Technology-Iraq, Baghdad, Iraq

    Alaa Q. Raheema

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  1. Alaa Q. Raheema
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AQR contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

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Correspondence to Alaa Q. Raheema.

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Raheema, A.Q. Challenges and vulnerability assessment of cybersecurity in IoT-enabled SC. Wireless Netw 30, 6887–6900 (2024). https://doi.org/10.1007/s11276-023-03493-4

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