Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices.However, these approaches face challenges including computation and storage costs, along with a single point of failure risk, threatening data privacy and availability. The widespread dispersion of data across interconnected devices underscores the necessity for decentralized approaches. This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs. FL-IDS reduces computation and storage costs for both clients and the central server, crucial for resource-constrained UAVs. Operating in a decentralized manner, FL-IDS enables UAVs to collaboratively train a global intrusion detection model without sharing raw data, thus avoiding the delay in decisions based on collected data, as is often the case with traditional methods. Experimental results demonstrate FL-IDS's competitive performance with Central IDS (C-IDS) while mitigating privacy concerns, with the Bias Towards Specific Clients (BTSC) method further enhancing FL-IDS performance even at lower attacker ratios. Comparative analysis with traditional intrusion detection methods, including Local IDS (L-IDS), sheds light on FL-IDS's strengths. This study significantly contributes to UAV security by introducing a privacy-aware, decentralized intrusion detection approach tailored to UAV networks. Moreover, by introducing a realistic dataset for FANETs and federated learning, our approach differs from others lacking high dynamism and 3D node movements or accurate federated data federations.

翻译：摘要：在飞行自组织网络（FANETs）中运行的无人机（UAVs）因其网络的动态性和分布式特性而面临安全挑战。以往研究主要聚焦于集中式入侵检测，假设存在一个中央实体负责存储和分析来自所有设备的数据。然而，这些方法面临计算与存储成本问题，以及单点故障风险，威胁数据隐私与可用性。数据在互联设备间广泛分散的现状凸显了分布式方法的必要性。本文提出了一种基于联邦学习的入侵检测系统（FL-IDS），以应对FANETs中集中式系统面临的挑战。FL-IDS降低了客户端和中央服务器的计算与存储成本，这对资源受限的无人机至关重要。该系统以分布式方式运行，使无人机能够在不共享原始数据的情况下协作训练全局入侵检测模型，从而避免了传统方法中基于收集数据做出决策所带来的延迟。实验结果表明，FL-IDS在缓解隐私问题的同时，其性能与集中式入侵检测系统（C-IDS）相当，且针对特定客户端的偏置方法（BTSC）即使在攻击者比例较低时也能进一步提升FL-IDS的性能。与传统入侵检测方法（包括本地入侵检测系统（L-IDS））的对比分析揭示了FL-IDS的优势。本研究通过引入一种专为无人机网络定制的隐私感知分布式入侵检测方法，显著推动了无人机安全领域的发展。此外，通过引入面向FANETs和联邦学习的真实数据集，我们的方法区别于其他缺乏高动态性、三维节点运动或准确联邦数据聚合的研究。