May 15, 2024, 4:43 a.m. | Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka

cs.LG updates on arXiv.org arxiv.org

arXiv:2301.08403v3 Announce Type: replace
Abstract: 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. …

abstract aerial arxiv augmentation challenges complexity cs.cr cs.lg cybersecurity data divergence environmental environments generative hardware identification interference replace stat.ap stat.ml type unmanned aerial vehicles vehicles work

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