"UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques"

"Abstract \/ Dataset DescriptionUAVIDS-2025 is a comprehensive benchmark dataset designed for evaluating intrusion detection systems (IDS) in UAV (Unmanned Aerial Vehicle) swarm networks. The dataset was generated through extensive simulations using the NS-3.24 network simulator, with realistic UAV mobility modeled by an extended BOID algorithm. It includes 122,171 labeled flow records across five traffic categories: Normal, Blackhole, Flooding, Sybil, and Wormhole attacks.Each data sample represents a network flow characterized by 22 features, grouped into connection, traffic volume, and performance metrics. The simulations were configured with IEEE 802.11ac wireless standards, AODV routing, and a Nakagami channel model to ensure realism. This dataset enables the evaluation of machine learning-based IDS under various scenarios, including imbalanced attack distributions and swarm mobility.The dataset supports research in:Supervised\/unsupervised intrusion detectionFederated learning and decentralized securityAdversarial robustness and synthetic data generationUAVIDS-2025 is intended to provide a reproducible, scalable, and diverse testbed for the research community working on the security of UAV networks."摘要 / 数据集描述 UAVIDS-2025是一款专为评估无人机（Unmanned Aerial Vehicle, UAV）集群网络中的入侵检测系统（Intrusion Detection System, IDS）而打造的综合性基准数据集。该数据集通过使用NS-3.24网络模拟器开展的大规模仿真生成，并采用扩展BOID算法对真实的无人机移动性进行建模。数据集共包含122,171条带标注的流量记录，涵盖五大流量类别：正常流量、黑洞攻击、泛洪攻击、Sybil攻击与虫洞攻击。每条数据样本对应一条网络流量，共包含22项特征，可划分为连接特征、流量体量特征与性能指标特征三大类。本次仿真配置采用IEEE 802.11ac无线标准、AODV路由协议与Nakagami信道模型，以确保场景的真实性。本数据集可用于评估基于机器学习的入侵检测系统，支持包括攻击分布不均衡与集群移动性在内的多种测试场景。本数据集可支撑以下研究方向：监督/无监督入侵检测、联邦学习与去中心化安全、对抗鲁棒性与合成数据生成。UAVIDS-2025旨在为从事无人机网络安全研究的学界同仁提供一个可复现、可扩展且具备多样性的测试平台。