This work addresses the pressing need for cybersecurity in Unmanned Aerial Vehicles (UAVs), particularly focusing on the challenges of identifying UAVs using radiofrequency (RF) fingerprinting in constrained environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based identification methods ineffective. To address these complications, the study introduces the rigorous use of one-shot generative methods for augmenting transformed RF signals, offering a significant improvement in UAV identification. This approach shows promise in low-data regimes, outperforming deep generative methods like conditional generative adversarial networks (GANs) and variational autoencoders (VAEs). The paper provides a theoretical guarantee for the effectiveness of one-shot generative models in augmenting limited data, setting a precedent for their application in limited RF environments. This research not only contributes to the cybersecurity of UAVs but also rigorously broadens the scope of machine learning techniques in data-constrained scenarios, which may include atypical complex sequences beyond images and videos.

翻译：本研究针对无人机网络安全领域的迫切需求，特别聚焦于在受限环境下利用射频指纹识别无人机时所面临的挑战。受环境干扰和硬件缺陷影响的射频信号具有复杂性和多变性，常导致传统射频识别方法失效。为应对这些难题，本研究引入严谨的单样本生成方法对变换后的射频信号进行数据增强，显著提升了无人机识别性能。该方法在低数据场景下展现出优势，性能优于条件生成对抗网络和变分自编码器等深度生成方法。本文为单样本生成模型在扩充有限数据方面的有效性提供了理论保证，为其在有限射频环境中的应用开创了先河。这项研究不仅有助于提升无人机网络安全水平，更严格拓展了数据受限场景下机器学习技术的应用范畴——可涵盖图像视频之外的非常规复杂序列数据。