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Volume 9
Issue 12
10.3390/drones9120828
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks

by
Bo Xu
1,*,
Zhiqing Liu
1,
Zhongjun Dong
1,
Kaiqi Huang
1,
Xiaopeng Huang
1,
Haolin Zhu
1,2,
Jun Wei
1,
Yong Li
1,3,
Yangbai Zhang
1,3 and
Xiuping Li
1,3
1
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
2
Control System Research Company of AECC, Wuxi 214024, China
3
Data Communication Technology Research Institute, Beijing 100089, China
*
Author to whom correspondence should be addressed.
Drones 2025, 9(12), 828; https://doi.org/10.3390/drones9120828 (registering DOI)
Submission received: 24 October 2025 / Revised: 22 November 2025 / Accepted: 24 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)
Versions Notes

Abstract

Time-series regression models are essential components in unmanned aerial vehicles (UAVs) for accurate trajectory and state prediction. Nevertheless, they are still vulnerable to hybrid adversarial attacks, which can lead to a compromised mission performance and cause huge economic loss. For this challenge, we propose the Distribution-driven Perturbation-Adaptive Defense (DPAD) framework. DPAD improves perturbation detection with Gaussian Mixture Model (GMM)-based feature augmentation that raises the accuracy of perturbation strength prediction, increasing from 0.685 to 0.943 R2, and dynamically chooses a suitable defense sub-model or the original model for adaptive correction. The experiments on UAV_Delivery show that DPAD significantly enhances robustness by achieving about 80% reduction in prediction errors under hybrid attacks while maintaining high accuracy on clean samples with an inference speed of 2.744 ms per sample. The proposed framework can scale up an effective solution to defend UAV time-series regression models against complex adversarial scenarios.
Keywords: unmanned aerial vehicles (UAVs); adversarial training; time-series regression; model ensemble; Gaussian Mixture Model; robustness unmanned aerial vehicles (UAVs); adversarial training; time-series regression; model ensemble; Gaussian Mixture Model; robustness

Share and Cite

MDPI and ACS Style

Xu, B.; Liu, Z.; Dong, Z.; Huang, K.; Huang, X.; Zhu, H.; Wei, J.; Li, Y.; Zhang, Y.; Li, X. DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks. Drones 2025, 9, 828. https://doi.org/10.3390/drones9120828

AMA Style

Xu B, Liu Z, Dong Z, Huang K, Huang X, Zhu H, Wei J, Li Y, Zhang Y, Li X. DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks. Drones. 2025; 9(12):828. https://doi.org/10.3390/drones9120828

Chicago/Turabian Style

Xu, Bo, Zhiqing Liu, Zhongjun Dong, Kaiqi Huang, Xiaopeng Huang, Haolin Zhu, Jun Wei, Yong Li, Yangbai Zhang, and Xiuping Li. 2025. "DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks" Drones 9, no. 12: 828. https://doi.org/10.3390/drones9120828

APA Style

Xu, B., Liu, Z., Dong, Z., Huang, K., Huang, X., Zhu, H., Wei, J., Li, Y., Zhang, Y., & Li, X. (2025). DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks. Drones, 9(12), 828. https://doi.org/10.3390/drones9120828

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