Analyzing The FlySafe System Face to Malicious UAVs Playing Black Hole Attacks

• Agnaldo Batista UFPR
• Aldri Santos UFPR / UFMG

DOI: https://doi.org/10.5753/sbseg.2026.29340

Resumo

Many of UAV networks demand dynamic structural topological services, such as sharing UAV location information, to provide UAV-coordinated and safe flight. More than just fault-tolerant, this basic service must be resilient to black hole attacks that aim corrupt and damage the UAV network topology by ignoring the neighborhood information. This work analyzes the resilience of the FlySafe system for protecting UAV networks against malicious UAV performing black hole attacks when they disregard sharing location information about its neighbors in a swarm environment. The FlySafe system relies on opportunistic approaches like epidemic and direct delivery, and enhancing neighbor discovery process. A simulation evaluation with NS3 showed the security of the location service, particularly in detecting and segregating black hole attacks from network operation with an average accuracy of 96%. Further, FlySafe exhibited an improvement in overall neighborhood perception in about 49.9%.

Referências

Administration, F. A. (2021). Operation of Small Unmanned Aircraft Systems Over People. [link]. [Online]. Accessed: Aug, 2023.

Alipour-Fanid, A., Dabaghchian, M., Wang, N., Wang, P., Zhao, L., and Zeng, K. (2019). Machine learning-based delay-aware UAV detection and operation mode identification over encrypted Wi-Fi traffic. IEEE Transactions on Information Forensics and Security, 15:2346–2360.

Aviation, F. A. (2024). Section 3. Class G Airspace. Available at: [link]. [Online]. Accessed: Jul, 2026.

Batista, A. and Santos, A. (2025). Resilient UAVs location sharing service based on information freshness and opportunistic deliveries. Pervasive and Mobile Computing, 111:102066.

Batista, A. D. S. and Dos Santos, A. L. (2024). A survey on resilience in information sharing on networks: Taxonomy and applied techniques. ACM Computing Surveys, 56(12):1–36.

Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sensing, 13(6):1204. DOI: 10.3390/rs13061204.

Farooq, J. and Zhu, Q. (2025). Cyber Resilience in Next-Generation Networks: Threat Landscape, Theoretical Foundations, and Design Paradigms. arXiv preprint arXiv:2512.22721.

GVR, N. G. T. R. T. (2026). Drone Market (2026 - 2033). [link]. [Online]. Accessed: May, 2026.

Hadi, H. J., Cao, Y., Li, S., Hu, Y., Wang, J., and Wang, S. (2024). Real-time collaborative intrusion detection system in UAV networks using deep learning. IEEE Internet of Things Journal, 11(20):33371–33391.

Hernandez, M., Gur, G., Tangade, S., and Namuduri, K. (2023). Security for Vehicle-to-Vehicle Communications for Unmanned Aircraft Systems. Security for Vehicle-to-Vehicle Communications for Unmanned Aircraft Systems, (10085742):1–24.

Hesford, J., Cheng, D., Wan, A., Huynh, L., Kim, S., Kim, H., and Hong, J. B. (2024). Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice. arXiv preprint arXiv:2403.17458. DOI: 10.48550/arXiv.2403.17458.

Huang, H., Su, J., and Wang, F.-Y. (2024). The Potential of Low-Altitude Airspace: The Future of Urban Air Transportation. IEEE Transactions on Intelligent Vehicles, 9(8):5250–5254.

ICAO (2018). ADS-B Implementation and Operations Guidance Document - Edition 11. [link]. [Online]. Accessed: Jul, 2021.

Kramar, V. (2020). UAS (drone) in Response to Coronavirus. In 27th Conference of Open Innovations Association, FRUCT, pages 90–100, New Jersey, USA. IEEE.

Lakew, D. S., Sa’ad, U., Dao, N.-N., Na, W., and Cho, S. (2020). Routing in Flying Ad Hoc Networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 22(2):1071–1120. DOI: 10.1109/COMST.2020.2982452.

Lamport, L. (2019). Time, clocks, and the ordering of events in a distributed system. In Concurrency: the Works of Leslie Lamport, pages 179–196. ACM.

Lima, R. R. and Pereira, G. A. (2021). On the Development of a Tether-based Drone Localization System. In International Conference on Unmanned Aircraft Systems, ICUAS, pages 195–201. IEEE. DOI: 10.1109/ICUAS51884.2021.9476778.

Maatouk, A., Kriouile, S., Assaad, M., and Ephremides, A. (2020). The Age of Incorrect Information: A New Performance Metric for Status Updates. IEEE/ACM Transactions on Networking, 28(5):2215–2228. DOI: 10.1109/TNET.2020.3005549.

NS-3, C. (2022). NS-3 Discrete-event Network Simulator. [link]. [Online]. Accessed: Feb, 2022.

Qureshi, K. N., Nafea, H., Javed, I. T., and Ghafoor, K. Z. (2024). Blockchain-based trust and authentication model for detecting and isolating malicious nodes in flying ad hoc networks. IEEE Access, 12:95390–95401.

Ren, S., Li, D., Hu, Q., Liu, Y., and Liu, J. (2022). An Improved Security OLSR Protocol against Black Hole Attack based on FANET. In 13th Asian Control Conference, ASCC, pages 383–388, New Jersey, USA. IEEE.

Sedjelmaci, H., Senouci, S. M., and Ansari, N. (2017). A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9):1594–1606.

Tian, B., Wang, L., Xu, L., Pan, W., Wu, H., Li, L., and Han, Z. (2023). UAV-Assisted Wireless Cooperative Communication and Coded Caching: A Multiagent Two-Timescale DRL Approach. IEEE Transactions on Mobile Computing, 23(5).

Tlili, F., Ayed, S., and Fourati, L. C. (2024). Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS). Computers & Security, 142:103878.

Tsao, K.-Y., Girdler, T., and Vassilakis, V. G. (2022). A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks. Ad Hoc Networks, 133(102894):1–39. DOI: 10.1016/j.adhoc.2022.102894.

Wang, L., Chen, Y., Wang, P., and Yan, Z. (2021). Security Threats and Countermeasures of Unmanned Aerial Vehicle Communications. IEEE Communications Standards Magazine, 5(4):41–47. DOI: 10.1109/MCOMSTD.0001.2000078.

Yang, J., Liu, X., Jiang, X., Zhang, Y., Chen, S., and He, H. (2023). Toward trusted unmanned aerial vehicle swarm networks: A blockchain-based approach. IEEE Vehicular Technology Magazine, 18(2):98–108.

Zhang, Y., Zhang, B., and Yi, X. (2018). Adaptive Data Sharing Algorithm for Aerial Swarm Coordination in Heterogeneous Network Environments (Short Paper). In International Conference on Collaborative Computing: Networking, Applications and Worksharing, volume 268 of CollaborateCom, pages 202–210. Springer, Cham. DOI: 10.1007/978-3-030-12981-1_14.

Zhou, Y. (2025). Unmanned aerial vehicles based low-altitude economy with lifecycle techno-economic-environmental analysis for sustainable and smart cities. Journal of Cleaner Production, 499:145050.