标题： 基于联邦学习的轻量级网络与零信任用于无人机认证 显示英文标题

标题： A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication

摘要： 无人机（UAV）正越来越多地被集成到下一代网络中，以增强通信覆盖范围和网络容量。 然而，无人机的动态性和移动性带来了重大的安全挑战，包括干扰、窃听和网络攻击。 为解决这些安全挑战，本文提出了一种基于联邦学习的轻量级网络，采用零信任策略以增强无人机网络的安全性。 提出了一种新颖的轻量级频谱图网络用于无人机认证和拒绝，该网络可以根据频谱图有效认证和拒绝无人机。 实验表明，LSNet在识别已知和未知无人机类别方面表现出色，与现有基准相比，在准确性、模型紧凑性和存储需求方面均有显著提升。 值得注意的是，当使用所有五个客户端进行训练时，LSNet对于已知无人机类型的准确率超过$80\%$，对于未知类型的接收者操作特征曲线下的面积（AUROC）为$0.7$。 进一步的分析探讨了客户端数量和未知无人机存在对性能的影响，强化了我们所提出的框架在现实世界联邦学习场景中的实用性和有效性。 显示英文摘要

摘要： Unmanned aerial vehicles (UAVs) are increasingly being integrated into next-generation networks to enhance communication coverage and network capacity. However, the dynamic and mobile nature of UAVs poses significant security challenges, including jamming, eavesdropping, and cyber-attacks. To address these security challenges, this paper proposes a federated learning-based lightweight network with zero trust for enhancing the security of UAV networks. A novel lightweight spectrogram network is proposed for UAV authentication and rejection, which can effectively authenticate and reject UAVs based on spectrograms. Experiments highlight LSNet's superior performance in identifying both known and unknown UAV classes, demonstrating significant improvements over existing benchmarks in terms of accuracy, model compactness, and storage requirements. Notably, LSNet achieves an accuracy of over $80\%$ for known UAV types and an Area Under the Receiver Operating Characteristic (AUROC) of $0.7$ for unknown types when trained with all five clients. Further analyses explore the impact of varying the number of clients and the presence of unknown UAVs, reinforcing the practical applicability and effectiveness of our proposed framework in real-world FL scenarios.