Collaborative Intrusion Detection System for Unmanned Aerial Vehicles Swarm Security

• Leandro Marcos da Silva USP
• Kalinka R. L. J. C. Branco USP

DOI: https://doi.org/10.5753/ctd.2025.8273

Resumo

Unmanned Aerial Vehicles (UAVs) are increasingly used in military, civil, and commercial applications, but they are vulnerable to cyberattacks that threaten security and privacy. These threats include signal interception, unauthorized access, data theft, and remote control of UAVs. An Intrusion Detection System (IDS) is used to mitigate these risks. However, most IDS focus on individual data sources, failing to detect swarm-specific threats. This work presents REMY, a collaboRative intrusion dEtection system for unManned aerial vehicles swarm SecuritY that detects network and in-flight anomalies using machine learning techniques. REMY identifies network attacks such as blackhole, grayhole, and flooding, as well as in-flight threats like GPS spoofing and jamming. Federated learning is applied in model training to ensure data privacy and collaboration. The system is designed to be hardware-independent and lightweight, with low energy consumption and efficient use of resources.

Referências

Ahmed, M., Cox, D., Simpson, B., and Aloufi, A. (2022). Ecu-ioft: A dataset for analysing cyber-attacks on internet of flying things. Applied Sciences, 12(4):1990.

Almomani, I., Al-Kasasbeh, B., and Al-Akhras, M. (2016). Wsn-ds: A dataset for intrusion detection systems in wireless sensor networks. Journal of Sensors, 2016.

Basan, E., Lapina, M., Mudruk, N., and Abramov, E. (2021). Intelligent intrusion detection system for a group of uavs. In Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part II 12, pages 230–240. Springer.

Choudhary, G., Sharma, V., You, I., Yim, K., Chen, R., and Cho, J.-H. (2018). Intrusion detection systems for networked unmanned aerial vehicles: A survey. In 2018 14th International Wireless Communications & Mobile Computing Conference, pages 560–565. IEEE.

He, X., Chen, Q., Tang, L., Wang, W., and Liu, T. (2022). Cgan-based collaborative intrusion detection for uav networks: A blockchain-empowered distributed federated learning approach. IEEE Internet of Things Journal, 10(1):120–132.

Park, K. H., Park, E., and Kim, H. K. (2020). Unsupervised intrusion detection system for unmanned aerial vehicle with less labeling effort. In International Conference on Information Security Applications, pages 45–58. Springer.

Ramadan, R. A., Emara, A.-H., Al-Sarem, M., and Elhamahmy, M. (2021). Internet of drones intrusion detection using deep learning. Electronics, 10(21):2633.

Whelan, J., Almehmadi, A., and El-Khatib, K. (2022). Artificial intelligence for intrusion detection systems in unmanned aerial vehicles. Computers and Electrical Engineering, 99:107784.

Xue, Z., Wang, J., Ding, G., Zhou, H., and Wu, Q. (2018). Maximization of data dissemination in uav-supported internet of things. IEEE Wireless Communications Letters, 8(1):185–188.

Zaidi, S., Atiquzzaman, M., and Calafate, C. T. (2021). Internet of flying things (ioft): A survey. Computer Communications, 165:53–74.

Zhang, R., Condomines, J.-P., and Lochin, E. (2022). A multifractal analysis and machine learning based intrusion detection system with an application in a uas/radar system. Drones, 6(1):21.