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.