The rapid expansion of smart cities has led to the widespread deployment of Internet of Things (IoT) devices for real-time data collection and urban optimization. However, these interconnected systems face critical cybersecurity risks, including data tampering, unauthorized access, and privacy breaches. This paper proposes a blockchain-based framework designed to enhance the security, integrity, and resilience of IoT data in smart city environments. Leveraging a private blockchain, the system ensures decentralized, tamper-proof data storage, and transaction verification through digital signatures and a lightweight Proof of Work consensus mechanism. Smart contracts are employed to automate access control and respond to anomalies in real time. A Python-based simulation demonstrates the framework’s effectiveness in securing IoT communications. The system supports rapid transaction validation with minimal latency and enables timely detection of anomalous patterns through integrated machine learning. Evaluations show that the framework maintains consistent performance across diverse smart city components such as transportation, healthcare, and building security. These results highlight the potential of the proposed solution to enable secure, scalable, and real-time IoT ecosystems for modern urban infrastructures.

1. Aggarwal, S., & Kumar, N. (2021). Basics of blockchain. In: Advances in Computers. Elsevier. Volume 121. pp. 129–146.
   https://doi.org/10.1016/bs.adcom.2020.08.007

2. Ali, J., Singh, S. K., Jiang W., et al. (2025). A deep dive into cybersecurity solutions for AI-Driven Iot-Enabled smart cities in Advanced Communication Networks. Computer Communications, 229, 108000. https://doi.org/10.1016/j.comcom.2024.108000

3. Alizadeh, M., Andersson, K., & Schelén, O. (2022). Comparative analysis of decentralized identity approaches. IEEE Access, 10, 92273–92283. https://doi.org/10.1109/ACCESS.2022.3202553

4. Alzahrani, A. I., Chauhdary, S. H., & Alshdadi, A. A. (2023). Internet of Things (IoT)-Based wastewater management in Smart Cities. Electronics, 12(12), 2590. https://doi.org/10.3390/electronics12122590

5. Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., et al. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333. https://doi.org/10.3390/electronics12061333

6. Baucas, M. J., & Spachos, P. (2021). Permissioned blockchain reinforced API platform for data management in IoT-based sensor networks. In: 2021 IEEE Global Communications Conference (GLOBECOM). IEEE. pp. 1–6. https://doi.org/10.1109/
   GLOBECOM46510.2021.9685837

7. De Guimarães, J. C. F., Severo, E. A., Júnior, L. A. F., et al. (2020). Governance and quality of life in smart cities: Towards sustainable development goals. Journal of Cleaner Production, 253, 119926. https://doi.org/10.1016/j.jclepro.2020.119926

8. Hasan, M. K., Alkhalifah, A., Islam, S., et al. (2022). Blockchain technology on smart grid, energy trading, and big data: Security issues, challenges, and recommendations. Wireless Communications and Mobile Computing 2022, 9065768. https://doi.org/10.1155/2022/9065768

9. Hashem, I. A., Siddiqa, A., Alaba, F. A., et al. (2024). Distributed intelligence for IoT-Based smart cities: A survey. Neural Computing and Applications, 1–36. https://doi.org/10.1007/s00521-024-10136-y

10. Houichi, M., Jaidi, F., & Bouhoula, A. (2021). A systematic approach for IoT cyber-attacks detection in smart cities using machine learning techniques. In: International Conference on Advanced Information Networking and Applications. Springer. pp. 215–228. https://doi.org/10.1007/978-3-030-75075-6_17

11. Houichi, M., Jaidi, F., & Bouhoula, A. (2022). Analysis of smart cities security: Challenges and advancements. In: 2022 15^th 
    International Conference on Security of Information and Networks (SIN). IEEE. pp. 1–5. https://doi.org/
    10.1109/SIN56466.2022.9970494

12. Houichi, M., Jaidi, F., & Bouhoula, A. (2023). A comprehensive study of intrusion detection within Internet of Things-based smart cities: Synthesis, analysis and a novel approach. In: 2023 International Wireless Communications and Mobile Computing (IWCMC). IEEE. pp. 505–511. https://doi.org/10.1109/IWCMC58020.2023.10182948

13. Houichi, M., Jaidi, F., & Bouhoula, A. (2024). Cyber security within smart cities: A comprehensive study and a novel
    intrusion detection-based approach. Computers, Materials and Continua, 81(1), 393–441. https://doi.org/10.32604/
    cmc.2024.054007

14. Jianping, W., Guangqiu, Q., Chunming, W., et al. (2024). Federated learning for network attack detection using Attention-Based graph neural networks. Scientific Reports, 14(1), 19088. https://doi.org/10.1038/s41598-024-70032-2

15. Kahan, J. H., Allen, A. C., & George, J. K. (2009). An operational framework for resilience. Journal of Homeland Security and Emergency Management, 6(1), 83. https://doi.org/10.2202/1547-7355.1675

16. Khare, A., Merlino, G., Longo, F., et al. (2020). Design of a trustless smart city system: The #SmartME experiment. Internet of Things, 10, 100126. https://doi.org/10.1016/j.iot.2019.100126

17. Khan, A. A., Laghari, A. A., Shaikh, Z. A., et al. (2022). Internet of Things (IoT) security with blockchain technology:
    A state-of-the-Art review. IEEE Access, 10, 122679–122695. https://doi.org/10.1109/ACCESS.2022.3223370

18. Khan, M. A., & Salah, K. (2018). IoT Security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022

19. Khanam, S., Ahmedy, I. B., Idris, M. Y. I., et al. (2020). A survey of security challenges, attacks taxonomy and advanced countermeasures in the Internet of Things. IEEE Access, 8, 219709–219743. https://doi.org/10.1109/ACCESS.2020.3037359

20. Liu, Y., Qian, K., Wang, K., et al. (2021). Effective scaling of blockchain beyond consensus innovations and Moore’s Law: Challenges and opportunities. IEEE Systems Journal, 16(1), 1424–1435. https://doi.org/10.1109/JSYST.2021.3087798

21. Lunardi, R. C., Alharby, M., Nunes, H. C., et al. (2020). Context-based consensus for appendable-block blockchains. In: 2020 IEEE International Conference on Blockchain (Blockchain). IEEE. pp. 401–408. https://doi.org/10.1109/Blockchain50366.
    2020.00058

22. Mahmood, A., Khan, A., Anjum, A., et al. (2023). An efficient and Privacy-Preserving Blockchain-Based secure data aggregation
    in smart grids. Sustainable Energy Technologies and Assessments, 60, 103414. https://doi.org/10.1016/j.seta.2023.103414

23. Majeed, U., Khan, L. U., Yaqoob, I., et al. (2021). Blockchain for IoT-Based smart cities: Recent advances, requirements, and future challenges. Journal of Network and Computer Applications, 181, 103007. https://doi.org/10.1016/j.jnca.2021.103007

24. Morchid, A., Jebabra, R., Ismail, A., et al. (2024). IoT-Enabled fire detection for sustainable agriculture: A Real-Time system using flask and embedded technologies. Results in Engineering, 23, 102705. https://doi.org/10.1016/j.rineng.2024.102705

25. Paolone, G., Iachetti, D., Paesani, R., et al (2022). A holistic overview of the Internet of Things Ecosystem. IoT, 3(4), 398–434. https://doi.org/10.3390/iot3040022

26. Pieroni, A., Scarpato, N., Di Nunzio, L., et al. (2018). Smarter city: Smart energy grid based on blockchain technology.
    International Journal of Advanced Science, Engineering and Information Technology, 8(1), 298–306. https://doi.org/
    10.18517/ijaseit.8.1.4954

27. Rahardja, U., Hidayanto, A. N., Lutfiani, N., et al. (2021). Immutability of distributed hash model on blockchain node storage. Scientific Journal of Informatics, 8(1), 137–143. https://doi.org/10.15294/sji.v8i1.29444

28. Rahman, M., & Saifullah, A. (2022). Transparent and Tamper-Proof event ordering in the Internet of Things platforms. IEEE Internet of Things Journal, 10(6), 5335–5348. https://doi.org/10.1109/JIOT.2022.3222450

29. Rahman, M. A., Rashid, M. M., Hossain, M. S., et al. (2021). Blockchain and IoT-Based cognitive edge framework for sharing economy services in a smart city. IEEE Access, 7, 18611–18621. https://doi.org/10.1109/ACCESS.2019.2896065

30. Rahman, M. A., Hossain, M. S., Loukas, G., et al. (2018). Blockchain-Based mobile edge computing framework for secure
    therapy Applications. IEEE Access, 6, 72469–72478. https://doi.org/10.1109/ACCESS.2018.2881246

31. Rathore, S., Pan, Y., & Park, J. H. (2019). BlockDeepNet: A blockchain-Based secure deep learning for IoT network.
    Sustainability, 11(14), 3974. https://doi.org/10.3390/su11143974

32. Scekic, O., Nastic, S., & Dustdar, S. (2018). Blockchain-Supported smart city platform for social value Co-Creation and exchange. IEEE Internet Computing, 23(1), 19–28. https://doi.org/10.1109/MIC.2018.2881518

33. Shahat Osman, A. M., & Elragal, A. (2021). Smart cities and big data analytics: A data-driven decision-making use case. Smart Cities, 4(1), 286–313. https://doi.org/10.3390/smartcities4010018

34. Sirena, P., & Patti, F. P. (2022). Smart contracts and automation of private relationships. In: Constitutional challenges in the
    algorithmic society. Cambridge University Press. pp. 315–330. https://doi.org/10.1017/9781108914857.017

35. Telo, J. (2023). Smart city security threats and countermeasures in the context of emerging technologies. International Journal of Intelligent Automation and Computing, 6(1), 31–45.

36. Xie, J., Tang, H., Huang, T., et al. (2019). A Survey of blockchain technology applied to smart cities: Research issues and challenges. IEEE Communications Surveys & Tutorials, 21(3), 2794–2830. https://doi.org/10.1109/COMST.2019.2899617

37. Xihua, Z., & Goyal, S. (2022). Security and privacy challenges using IoT-Blockchain technology in a smart city: Critical analysis. International Journal of Electrical & Electronics Research, 10(2), 190–195. https://doi.org/10.37391/ijeer.100224

38. Yu, Z., Song, L., Jiang, L., et al. (2022). Systematic literature review on the security challenges of blockchain in IoT-Based
    smart cities. Kybernetes, 51(1), 323–347. https://doi.org/10.1108/K-07-2020-0449

39. Zang, X., Zheng, Z., Zheng, H., et al. (2025). HyperEye: A lightweight features fusion model for unknown encrypted malware traffic detection. IEEE Transactions on Consumer Electronics, 71(2), 5079–5089. https://doi.org/10.1109/TCE.2025.3558353