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

UAVIDS-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 detection Federated learning and decentralized security Adversarial robustness and synthetic data generation UAVIDS-2025 is intended to provide a reproducible, scalable, and diverse testbed for the research community working on the security of UAV networks. If you are using our dataset, you should cite our related paper which outlining the details of the dataset and its underlying principles: @inproceedings{zeng2025uavids, title={Uavids-2025: A benchmark dataset for intrusion detection in uav networks using machine learning techniques}, author={Zeng, Qingli and Bashir, Abdalrahman and Nait-Abdesselam, Farid}, booktitle={2025 IEEE Conference on Communications and Network Security (CNS)}, pages={1--9}, year={2025}, organization={IEEE}} @inproceedings{QingliFedGraph-ID, title={FedGraph-ID: A Federated Graph Learning Framework for Intrusion Detection in UAV Networks Under Adversarial Settings}, author={Zeng, Qingli and Fu, Yinjin and Nait-Abdesselam, Farid}, booktitle={IEEE INFOCOM 2026-IEEE Conference on Computer Communications}, year={2026}, organization={IEEE}}