Volume 18 , Issue 1 , PP: 70–85, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Andino Maseleno 1 * , Aa Hubur 2
Doi: https://doi.org/10.54216/JCIM.180105
Cyberattack detection in unmanned aerial vehicle environments has become an essential requirement for dependable digital operations. Security analytics for these environments should not only separate benign and malicious traffic, but should also provide interpretable evidence that can support timely triage and intervention. This paper presents a risk-aware classification framework for UAV communication security based on a leakage-screened feature design and a gradient-boosting ensemble model. The framework combines multiclass discrimination, probability-based decision logic, and feature-level interpretation within one coherent workflow. The study demonstrates that a carefully designed ensemble approach can provide balanced and operationally meaningful cyberattack recognition while remaining transparent enough for practical cybersecurity management. The results also show that communication-structure variables provide strong discriminatory power and that replay-type activity remains more difficult to separate than benign or denial-of-service behavior. The proposed framework therefore contributes a reproducible analytical design and a managerial reading of cyberattack classification for UAV operations.
UAV cybersecurity , Cyberattack analytics , Gradient boosting , Intrusion detection , Multiclass classification , Interpretable security
[1] Samuel Chase Hassler, Umair Ahmad Mughal, and Muhammad Ismail. Cyber-physical intrusion detection system for unmanned aerial vehicles. IEEE Transactions on Intelligent Transportation Systems, 25(6):6106–6117, 2024. doi: 10.1109/TITS.2023.3339728.
[2] Raghad A. AL-Syouf, Raed M. Bani-Hani, and Omar Y. AL-Jarrah. Machine learning approaches to intrusion detection in unmanned aerial vehicles (uavs). Neural Computing and Applications, 36:18009–18041, 2024. doi: 10.1007/s00521-024-10306-y.
[3] Fadhila Tlili, Samiha Ayed, and Lamia Chaari Fourati. Exhaustive distributed intrusion detection system for uavs attacks detection and security enforcement (e-dids). Computers & Security, 142:103878, 2024. doi: 10.1016/j.cose.2024.103878.
[4] Menna Gamal, Mohamed Elhamahmy, Sanaa Taha, and Hesham Elmahdy. Improving intrusion detection using lstm-rnn to protect drones’ networks. Egyptian Informatics Journal, 27: 100501, 2024. doi: 10.1016/j.eij.2024.100501.
[5] Bhawana Sharma, Lokesh Sharma, Chhagan Lal, and Satyabrata Roy. Explainable artificial intelligence for intrusion detection in iot networks: A deep learning based approach. Expert Systems with Applications, 238:121751, 2024. doi: 10.1016/j.eswa.2023.121751.
[6] Vivian Ukamaka Ihekoronye, Simeon Okechukwu Ajakwe, Jae Min Lee, and Dong-Seong Kim. Droneguard: An explainable and efficient machine learning framework for intrusion detection in drone networks. IEEE Internet of Things Journal, 12(7):7708–7722, 2025. doi: 10.1109/ JIOT.2024.3519633.
[7] Ahmad Burhan Mohammed and Lamia Chaari Fourati. Investigation on datasets toward intelligent intrusion detection systems for intra and inter-uavs communication systems. Computers & Security, 150:104215, 2025. doi: 10.1016/j.cose.2024.104215.
[8] Treepop Wisanwanichthan and Mason Thammawichai. A lightweight intrusion detection system for iot and uav using deep neural networks with knowledge distillation. Computers, 14 (7):291, 2025. doi: 10.3390/computers14070291.
[9] D. Manivannan. Recent endeavors in machine learning-powered intrusion detection systems for the internet of things. Journal of Network and Computer Applications, 229:103925, 2024. doi: 10.1016/j.jnca.2024.103925.
[10] Euclides Carlos Pinto Neto, Sajjad Dadkhah, Raphael Ferreira, Alireza Zohourian, Rongxing Lu, and Ali A. Ghorbani. Ciciot2023: A real-time dataset and benchmark for large-scale attacks in iot environment. Sensors, 23(13):5941, 2023. doi: 10.3390/s23135941.
