Blockchain-Powered Privacy Preservation and Attack Mitigation for 6g-Connected Vehicular Clouds

  • Authors

    https://doi.org/10.14419/k630rb63

    Received date: December 24, 2025

    Accepted date: January 18, 2026

    Published date: January 23, 2026

  • 6G Vehicular Cloud Networks; Blockchain-Based Security; Federated Learning; Trust Management; Intrusion Detection; Privacy Preserva‎tion; Smart Contracts; Proof-of-Trust Consensus; Intelligent Transportation Systems

  • Abstract

    The transition toward sixth-generation (6G) wireless communication is expected to significantly expand the scale, intelligence, and connec-‎tivity of vehicular cloud networks, while simultaneously intensifying their exposure to sophisticated cyber threats and privacy risks. Contin-‎uous exchange of vehicular telemetry, combined with ultra-low-latency communication and high mobility, renders conventional centralized ‎security and intrusion detection mechanisms inadequate. This paper presents a decentralized, privacy-aware security methodology for 6G-‎enabled vehicular cloud environments that integrates dynamic trust evaluation, federated anomaly detection, and blockchain-based enforce-‎ment.‎

    The proposed approach employs adaptive trust modeling to continuously assess vehicular behavior, federated learning to enable collabora-‎tive anomaly detection without disclosing raw data, and a lightweight Proof-of-Trust consensus mechanism to ensure low-latency, verifiable ‎decision-making. Automated mitigation is enforced through smart contracts, enabling rapid response and accountability without centralized ‎control. The methodology is evaluated using three widely adopted benchmark datasets—CIC-IDS2017, TON_IoT, and N-BaIoT—that ‎cover volumetric attacks, stealthy intrusions, and coordinated botnet behavior.‎

    Experimental results demonstrate detection rates exceeding 95% across all datasets, with false-positive rates of nearly 1%. End-to-end detec-‎tion-to-mitigation latency remains below 20 ms under high vehicular density, satisfying 6G ultra-reliable low-latency communication re-‎quirements. A comparative analysis reveals that the proposed approach outperforms traditional signature-based and learning-based intrusion ‎detection systems in terms of accuracy, scalability, and enforcement capability, while maintaining data privacy through federated learning ‎and differential privacy mechanisms.‎

    These results confirm that decentralized trust management, privacy-preserving intelligence, and automated blockchain enforcement can be ‎jointly realized in 6G vehicular cloud systems. The proposed methodology provides a practical, scalable foundation for securing next-‎generation intelligent transportation infrastructure against evolving cyber threats‎.

  • References

    1. A. Dorri, M. Steger, S. S. Kanhere, and R. Jurdak, “Blockchain: A distributed solution to automotive security and privacy,” arXiv preprint arXiv:1704.00073, 2017. https://doi.org/10.1109/MCOM.2017.1700879.
    2. R. Chen, J. Guo, and F. Li, “Blockchain-based trust management for Internet of Vehicles,” IEEE Access, vol. 8, pp. 119 950–119 960, 2020.
    3. L. Xu, L. Chen, and X. Liu, “Lightweight blockchain consensus for high-speed vehicular networks,” IEEE Trans. Veh. Technol., vol. 72, no. 2, pp. 1775–1789, 2023.
    4. M. Rehman, H. Ahmad, and Z. Khan, “Hybrid PBFT-Proof of Trust for blockchain-enabled VANETs,” IEEE Access, vol. 10, pp. 132 445–132 460, 2022.
    5. J. Kang, R. Yu, X. Huang, and Y. Zhang, “SDN-assisted blockchain for decentralized vehicular networks,” IEEE IoT J., vol. 8, no. 12, pp. 9823–9835, 2022.
    6. M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “A tutorial on 6G communications: Vision, enabling technologies, and new frontiers,” IEEE Commun. Surv. Tutor., vol. 23, no. 3, pp. 1681–1722, 2021.
    7. S. Li, K. Ota, and M. Dong, “Location privacy protection for Internet-of-Vehicles in 6G networks,” IEEE Network, vol. 37, no. 1, pp. 55–61, 2023.
    8. X. Zhang, Y. Liu, and H. Zhao, “Adversarial data poisoning in 6G vehicular federated learning,” IEEE Trans. Inf. Forensics Secur., vol. 18, pp. 985–999, 2023.
    9. H. Lu, J. Li, and X. Liu, “A comprehensive survey on cyberattacks in vehicular networks,” Vehicular Communications, vol. 35, 2024.
    10. Y. Dai, W. Chen, and Q. He, “Edge-intelligent intrusion detection for 6G vehicular IoT,” IEEE IoT J., vol. 11, no. 9, pp. 16534–16547, 2024.
    11. W. Yu, F. Xiao, and L. Zhou, “Blockchain-AI fusion for 6G vehicular trust management,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 4, pp. 4381–4395, 2024.
    12. “Framework of data privacy preservation and location obfuscation in vehicular cloud networks,” Concurrency Comput.: Pract. Exper., vol. 34, no. 5, e6682, 2022. https://doi.org/10.1002/cpe.6682.
    13. “Hilbert curves-based location privacy technique for vehicular cloud networks,” Cluster Computing, vol. 27, pp. 2489–2504, 2024. https://doi.org/10.1007/s10586-023-04068-w.
    14. J. Jiang, X. Shen, and C. Lin, “Blockchain-enabled secure message verification for 6G vehicular networks,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 7, pp. 5043–5057, 2024.
    15. S. Mahmood, R. Iqbal, and N. Malik, “Privacy-preserving access control for 6G UAV-vehicular networks,” Vehicular Communications, vol. 38, 2025.
    16. A. Al-Matari, S. Al-Gumaei, and H. Al-Qasem, “Blockchain-assisted spectrum sharing in 6G cognitive vehicular IoT,” IEEE Access, vol. 12, pp. 190 450–190 467, 2024.
    17. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” Proc. Int. Conf. Inf. Syst. Security Privacy (ICISSP), 2018, pp. 108–116. https://doi.org/10.5220/0006639801080116.
    18. N. Moustafa, B. Turnbull, and K.-K. R. Choo, “An ensemble intrusion detection technique based on proposed statistical flow features for protect-ing network traffic of Internet of Things,” IEEE Internet Things J., vol. 6, no. 3, pp. 4815–4830, Jun. 2019. https://doi.org/10.1109/JIOT.2018.2871719.
    19. Y. Meidan et al., “N-BaIoT: Network-based detection of IoT botnet attacks using deep autoencoders,” IEEE Pervasive Computing, vol. 17, no. 3, pp. 12–22, Jul.–Sep. 2018. https://doi.org/10.1109/MPRV.2018.03367731.

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  • How to Cite

    Al-Balasmeh, H. (2026). Blockchain-Powered Privacy Preservation and Attack Mitigation for 6g-Connected Vehicular Clouds. International Journal of Basic and Applied Sciences, 15(1), 110-123. https://doi.org/10.14419/k630rb63