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  3. Volume 10, Number 1, 2025
  4. Using the Raft Algorithm to Coordinate Interceptor Drones in a UAV Detection and Neutralization System

Using the Raft Algorithm to Coordinate Interceptor Drones in a UAV Detection and Neutralization System

ACPS.
2025;
Volume 10, Number 1
: pp. 105 - 110
https://doi.org/10.23939/acps2025.01.105
Authors:
  1. A. O. Nyzhnyk
  2. A. I. Partyka
  3. Michal Podpora
1
Lviv Polytechnic National University, Department of Information Protection
2
Lviv Polytechnic National University, Department of Information Protection
3
University of Opole

This article explores the use of the Raft consensus algorithm to coordinate interceptor drones in systems designed to detect and neutralize unmanned aerial vehicles (UAVs). The modified Raft algorithm has enabled stable and synchronized drone actions, allowing for autonomous target interception. Modeling and simulation confirmed the system’s fault tolerance and real-time coordination capabilities. In scenarios involving partial communication failures or drone loss, the system has successfully maintained consensus and continued operation. The proposed architecture has used the Rust programming language to ensure memory safety and concurrency management. The results have provided the effectiveness of using Raft in distributed UAV defense systems, while offering advantages such as leader re-election, log replication, and secure communication channels. The paper also discusses cryptographic enhancements and system resilience to potential cyber threats. This research confirms the applicability of the Raft algorithm for UAV interceptor swarms and gives a foundation for further improvements in autonomous defense systems.

Raft consensus algorithm
interceptor drones
UAV detection and neutralization
distributed coordination
memory-safe programming
Rust language
real-time autonomous systems
cybersecurity in UAV networks
  1. Vora, S., Thakkar, N., & Gor, R. (2023). A study of performance measures and throughput of Raft consensus algorithm. International Journal of Research in Applied Science and Engineering Technology, 11, 862–869. DOI: https://doi.org/10.22214/ijraset.2023.54751.
  2. Chu, D., Zhao, C., Wang, R., Xiao, Q., Wang, W., & Cao, D. (2024). A survey of multi-vehicle consensus in uncertain networks for autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 25(12), 19319–19341. DOI: https://doi.org/10.1109/TITS.2024.3465046.
  3. [3] Abdorrahimi, B., Nekouie, A., Rahmani, A., Lansky, J., Nulíček, V., Hosseinzadeh, M., & Moattar, M. (2024). Blockchain technology and  raft consensus for secure physician prescriptions and improved  diagnoses  in electronic healthcare systems. Scientific Reports, 14, 2594. DOI: https://doi.org/10.1038/s41598-024-66746-y.
  4. Belous, R., & Krylov, Y. (2024). Minimisation of network traffic in the Raft-like consensus algorithm. Municipal Economy of Cities, 4, 2–6. DOI: https://doi.org/10.33042/ 2522-1809-2024-4-185-2-6.
  5. Zabunov, S., & Mardirossian, G. (2024). Four innovative drone interceptors. Proceedings of the Bulgarian Academy of Sciences, 77(2), 238–245. DOI: https://doi.org/10.7546/ CRABS.2024.02.09.
  6. Li, Y., Fan, Y., Zhang, L., & Crowcroft, J. (2023). Raft consensus reliability in wireless networks: Probabilistic analysis. IEEE Internet of Things Journal, 10(14), 12839– 12853. DOI: https://doi.org/10.1109/JIOT.2023.3257402.
  7. Huang, S., Yao, H., Wang, X., Mai, T., Wang, Z., & Guo, S. (2025). Graph attentional based agglomerative cluster for UAV swarm networks. IEEE Transactions on Network Science and Engineering, 1–18. DOI: https://doi.org/ 10.1109/TNSE.2025.3559215.
  8. Kumar, V., Asthana, A., & Tripathi, G. (2025). Enhancing data security in IoT-based UAV networks through blockchain integration. Engineering, Technology & Applied Science Research, 15(2), 21800–21804. DOI: https://doi.org/10.48084/etasr.9922.
  9. Mariani, S., Cabri, G., & Zambonelli, F. (2021). Coordination of autonomous vehicles: Taxonomy and survey. ACM Computing Surveys, 54(1), 1–33. DOI: https://doi.org/10.1145/3431231.
  10. Abdullayeva, F., & Valikhanli, O. (2024). A survey on UAVs security issues: Attack modeling, security aspects, countermeasures, open issues. Control and Cybernetics, 52(4),  405–439.  DOI:  https://doi.org/10.2478/candc-2023-0044.
  11. Culic, I., Vochescu, A., & Radovici, A. (2022). A low- latency  optimization  of  a  Rust-based  secure  operating system for embedded devices. Sensors, 22(22), 8700. DOI: https://doi.org/10.3390/s22228700.
  12. Oorschot, P. (2023). Memory errors and memory safety: A look at Java and Rust. IEEE Security & Privacy, 21(3), 62– 68. DOI: https://doi.org/10.1109/MSEC.2023.3249719.
  13. Hai, X., Qiu, H., Wen, C., & Feng, Q. (2023). A novel distributed situation awareness consensus approach for UAV swarm systems. IEEE Transactions on Intelligent Transportation Systems, 24(2), 14706–14717. DOI: https://doi.org/10.1109/TITS.2023.3300871.
  14. Yuan, H., Li, F., Diao, R., & Shu, T. (2024). Double‐layer Byzantine fault‐tolerant grouping consensus algorithm based on Raft. IET Blockchain, 4(6), 555–569. DOI: https://doi.org/10.1049/blc2.12073.
  15. Yang, H., Feng, Y., & Zhang, W. (2024). LRD-Raft: Log replication decouple for efficient and secure consensus in consortium blockchain-based IoT. IEEE Internet of Things Journal, 12(7), 8807-8820. DOI: https://doi.org/10.1109/ JIOT.2024.3506601.
  16. Cui, Y., Liang, Y., Luo, Q., Shu, Z., & Huang, T. (2024). Resilient consensus control of heterogeneous multi-UAV systems with leader of unknown input against Byzantine attacks. IEEE Transactions on Automation Science and Engineering, 22, 5388–5399. DOI: https://doi.org/10.1109/ TASE.2024.3420697.
  17. Tian, S., Zhang, C., & Bei, G. (2023). VSSB-Raft: A secure and efficient zero trust consensus algorithm for blockchain. ACM Transactions on Sensor Networks, 20(2), 1–22. DOI: https://doi.org/10.1145/3611308.
  18. Nyzhnyk, A., Partyka, A., & Podpora, M. (2024). Increase the cybersecurity of SCADA and IIoT devices with secure memory management. CEUR Workshop  Proceedings, 3800, 32–41.