Skip to main content
SpringerLink
Log in

Converging Blockchain and Deep Learning in UAV Network Defense Strategy: Ensuring Data Security During Flight

  • Conference paper
  • First Online:
Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14509))

Abstract

Unmanned Aerial Vehicles (UAVs) serve as highly versatile and efficient tools utilized across diverse industries for data collection purposes. However, they face vulnerabilities associated with wireless communication and data exchange, such as unauthorized access, data theft, and cyberattacks. These risks pose significant challenges to the establishment of reliable UAV network services. This study introduces a comprehensive blockchain-based architecture for UAV network services, designed to address these challenges. The proposed architecture tackles concerns related to identity authentication and privacy protection through the seamless integration of blockchain technology. Moreover, it incorporates advanced deep learning techniques to enhance UAV safety during operations and provide robust protection against cyber threats. A series of experimental tests were conducted, simulating various UAV network attack scenarios. The results of these experiments unequivocally demonstrate the feasibility and effectiveness of the blockchain-driven UAV network service architecture.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gupta, L., Jain, R., Vaszkun, G.: Survey of important issues in UAV communication networks. IEEE Commun. Surv. Tutorials 18(2), 1123–1152 (2015)

    Article  Google Scholar 

  2. Hayat, S., Yanmaz, E., Muzaffar, R.: Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun. Surv. Tutorials 18(4), 2624–2661 (2016)

    Article  Google Scholar 

  3. Zhang, Q., Jiang, M., Feng, Z., et al.: IoT enabled UAV: network architecture and routing algorithm. IEEE Internet Things J. 6(2), 3727–3742 (2019)

    Article  Google Scholar 

  4. Srivastava, A., Prakash, J.: Internet of low-altitude UAVs (IoLoUA): a methodical modeling on integration of internet of “Things’’ with “UAV’’ possibilities and tests. Artif. Intell. Rev. 56(3), 2279–2324 (2023)

    Article  Google Scholar 

  5. Datta, S.K., Dugelay, J.L., Bonnet, C.: IoT based UAV platform for emergency services. In: Proceedings of the International Conference on Information and Communication Technology Convergence, ICTC 2018, pp. 144–147 (2018)

    Google Scholar 

  6. Lihua, Z., Jianfeng, D., Yu, W., et al.: An online priority configuration algorithm for the UAV swarm in complex context. Procedia Comput. Sci. 150, 567–578 (2019)

    Article  Google Scholar 

  7. Dai, M., Huang, N., Wu, Y., et al.: Unmanned-aerial-vehicle-assisted wireless networks: advancements, challenges, and solutions. IEEE Internet Things J. 10(5), 4117–4147 (2022)

    Article  Google Scholar 

  8. Orsino, A., Ometov, A., Fodor, G., et al.: Effects of heterogeneous mobility on D2D-and drone-assisted mission-critical MTC in 5G. IEEE Commun. Mag. 55(2), 79–87 (2017)

    Article  Google Scholar 

  9. Peng, S., Zhou, F., Li, J., et al.: Efficient, dynamic and identity-based remote data integrity checking for multiple replicas. J. Netw. Comput. Appl. 134, 72–88 (2019)

    Article  Google Scholar 

  10. Strobel, V., Castelló, F.E., Dorigo, M.: Blockchain technology secures robot swarms: a comparison of consensus protocols and their resilience to byzantine robots. Front. Robot. AI 7, 54 (2020)

    Article  Google Scholar 

  11. Wen, Q., Gao, Y., Chen, Z., et al.: A blockchain-based data sharing scheme in the supply chain by IIoT. In: Proceedings of the IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019, pp. 695–700 (2019)

    Google Scholar 

  12. Gao, Y., Chen, Y., Hu, X., et al.: Blockchain based IIoT data sharing framework for SDN-enabled pervasive edge computing. IEEE Trans. Industr. Inf. 17(7), 5041–5049 (2020)

    Article  Google Scholar 

  13. Han, P., Sui, A., Wu, J.: Identity management and authentication of a UAV swarm based on a blockchain. Appl. Sci. 12(20), 10524 (2022)

    Article  Google Scholar 

  14. Millard, A.G., Timmis, J., Winfield, A.F.T.: Towards exogenous fault detection in swarm robotic systems. In: Natraj, A., Cameron, S., Melhuish, C., Witkowski, M. (eds.) TAROS 2013. LNCS, vol. 8069, pp. 429–430. Springer, Heidelberg (2013)

    Google Scholar 

  15. Castelló Ferrer, E.: The blockchain: a new framework for robotic swarm systems. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) FTC 2018. AISC, vol. 881, pp. 1037–1058. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02683-7_77

    Chapter  Google Scholar 

  16. Wang, J., Liu, Y., Niu, S., et al.: Lightweight blockchain assisted secure routing of swarm UAS networking. Comput. Commun. 165, 131–140 (2021)

    Article  Google Scholar 

  17. Nguyen, T., Katila, R., Gia, T.N.: A novel internet-of-drones and blockchain-based system architecture for search and rescue. In: Proceedings of the International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021, pp. 278–288 (2021)

    Google Scholar 

  18. Lee, S., Kim, M., Kim, M., et al.: Timely update probability analysis of blockchain ledger in UAV-assisted data collection networks. In: Proceedings of the International Conference on Communications, ICC 2022, pp. 4559–4564 (2022)

    Google Scholar 

  19. Ghribi, E., Khoei, T.T., Gorji, H.T., et al.: A secure blockchain-based communication approach for UAV networks. In: Proceedings of the International Conference on Electro Information Technology, EIT 2020, pp. 411–415 (2020)

    Google Scholar 

  20. Li, G., He, B., Wang, Z., et al.: Blockchain-enhanced spatiotemporal data aggregation for UAV-assisted wireless sensor networks. IEEE Trans. Industr. Inf. 18(7), 4520–4530 (2021)

    Article  Google Scholar 

  21. Safavat, S., Rawat, D.B.: OptiML: an enhanced ML approach towards design of SDN based UAV networks. In: Proceedings of the International Conference on Communications, ICC 2022, pp. 1–6 (2022)

    Google Scholar 

  22. Mershad, K.: PROACT: parallel multi-miner proof of accumulated trust protocol for internet of drones. Veh. Commun. 36, 100495 (2022)

    Google Scholar 

  23. Chen, J., Feng, Z., Wen, J.Y., et al.: A container-based DoS attack-resilient control framework for real-time UAV systems. In: Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, DATE 2019, pp. 1222–1227 (2019)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank the reviewers for their helpful comments and suggestions. This work was supported by the National Key Project of China (No. 2020YFB1005700).

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, 510006, China

    Zhihao Li, Qi Chen, Weichuan Mo, Xiaolin Wang & Li Hu

  2. State Key Laboratory of Integrated Service Networks (Xidian University), Xi’an, 710071, China

    Zhihao Li & Qi Chen

  3. Key Laboratory of High Confidence Software Technologies (MOE), School of Computer Science, Peking University, Beijing, 100871, China

    Yongzhi Cao

Authors
  1. Zhihao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weichuan Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yongzhi Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Chen .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Z., Chen, Q., Mo, W., Wang, X., Hu, L., Cao, Y. (2024). Converging Blockchain and Deep Learning in UAV Network Defense Strategy: Ensuring Data Security During Flight. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9785-5_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9784-8

  • Online ISBN: 978-981-99-9785-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation