Federated Learning for Intrusion Detection in UAV Networks

Deniz Berke Özsoy; Atakan Özcan; Mohammed Al-Hubaishi

doi:10.47000/tjmcs.1828534

Federated Learning for Intrusion Detection in UAV Networks

Abstract

UAV swarms operate under bandwidth constraints, intermittent connectivity, and privacy requirements that make fully centralized intrusion detection difficult. Using the UAVIDS--2025 benchmark, we construct a deployment-oriented federated intrusion detection setting by partitioning flows by source address into $K{=}176$ realistic, strongly non-IID clients, where each client approximates the traffic observed by an individual UAV. We train a compact multilayer perceptron ($22\!\rightarrow\!64\!\rightarrow\!32\!\rightarrow\!5$) across three federated optimizers (FedAvg, FedProx, and SCAFFOLD) and employ a leakage-safe feature pipeline that removes identifier and scenario fields while retaining mechanism-based traffic and QoS descriptors. Under a matched training and communication budget, FedAvg achieves $93\%$ accuracy, $93\%$ macro-F1, and AUROC $0.99$ on a held-out global test set, within roughly three percentage points of strong centralized baselines (XGBoost and LightGBM at $96\%$) that train on fully aggregated traffic. FedProx trades peak performance for more stable convergence ($86\%$ accuracy, $84\%$ macro-F1), whereas SCAFFOLD reduces communication but shows more pronounced classwise weaknesses ($82\%$ accuracy, $79\%$ macro-F1) under our current tuning. We report the communication footprint in MB per round and cumulatively, and we repeat the same held-out evaluation protocol on a second intrusion dataset (T-ITS) to assess transfer of optimizer trends. Overall, the results indicate that leakage-safe federated learning can keep raw UAV traffic local while recovering most of the detection quality of strong centralized models, clarifying accuracy--communication trade-offs for privacy-preserving IDS deployment in UAV swarms.

Keywords

References

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Details

Primary Language

English

Subjects

Information Security Management, Deep Learning, Neural Networks, Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Deniz Berke Özsoy ^*
0009-0004-3470-6295
Türkiye

Atakan Özcan
0009-0001-1819-7484
Türkiye

Mohammed Al-Hubaishi
0000-0002-9940-3592
Yemen

Publication Date

June 30, 2026

Submission Date

November 22, 2025

Acceptance Date

February 19, 2026

Published in Issue

Year 2026 Volume: 18 Number: 2

DOI

https://doi.org/10.47000/tjmcs.1828534

IZ

https://izlik.org/JA63GL69EF

Cite

RIS / Bibtex

APA

Özsoy, D. B., Özcan, A., & Al-Hubaishi, M. (2026). Federated Learning for Intrusion Detection in UAV Networks. Turkish Journal of Mathematics and Computer Science, 18(2), 481-503. https://doi.org/10.47000/tjmcs.1828534

AMA

1.Özsoy DB, Özcan A, Al-Hubaishi M. Federated Learning for Intrusion Detection in UAV Networks. TJMCS. 2026;18(2):481-503. doi:10.47000/tjmcs.1828534

Chicago

Özsoy, Deniz Berke, Atakan Özcan, and Mohammed Al-Hubaishi. 2026. “Federated Learning for Intrusion Detection in UAV Networks”. Turkish Journal of Mathematics and Computer Science 18 (2): 481-503. https://doi.org/10.47000/tjmcs.1828534.

EndNote

Özsoy DB, Özcan A, Al-Hubaishi M (June 1, 2026) Federated Learning for Intrusion Detection in UAV Networks. Turkish Journal of Mathematics and Computer Science 18 2 481–503.

IEEE

[1]D. B. Özsoy, A. Özcan, and M. Al-Hubaishi, “Federated Learning for Intrusion Detection in UAV Networks”, TJMCS, vol. 18, no. 2, pp. 481–503, June 2026, doi: 10.47000/tjmcs.1828534.

ISNAD

Özsoy, Deniz Berke - Özcan, Atakan - Al-Hubaishi, Mohammed. “Federated Learning for Intrusion Detection in UAV Networks”. Turkish Journal of Mathematics and Computer Science 18/2 (June 1, 2026): 481-503. https://doi.org/10.47000/tjmcs.1828534.

JAMA

1.Özsoy DB, Özcan A, Al-Hubaishi M. Federated Learning for Intrusion Detection in UAV Networks. TJMCS. 2026;18:481–503.

MLA

Özsoy, Deniz Berke, et al. “Federated Learning for Intrusion Detection in UAV Networks”. Turkish Journal of Mathematics and Computer Science, vol. 18, no. 2, June 2026, pp. 481-03, doi:10.47000/tjmcs.1828534.

Vancouver

1.Deniz Berke Özsoy, Atakan Özcan, Mohammed Al-Hubaishi. Federated Learning for Intrusion Detection in UAV Networks. TJMCS. 2026 Jun. 1;18(2):481-503. doi:10.47000/tjmcs.1828534