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

Highlights: What are the main findings? A novel Distribution-driven Perturbation-Adaptive Defense (DPAD) framework is proposed to enhance the robustness of UAV time-series regression models against hybrid adversarial attacks. The framework integrates a Gaussian Mixture Model (GMM)-based perturbation strength predictor with a dynamic defense selection mechanism, achieving adaptive correction under varying perturbation strengths. What are the implications of the main finding? DPAD significantly improves UAV model resilience and reliability in safety-critical missions, reducing prediction errors by up to 80% while maintaining real-time inference speed. The proposed approach provides a generalizable defense strategy for other deep learning-based time-series regression applications in aerial systems. 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 (Formula presented.), 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.