Quantum Machine Learning for UAV Swarm Intrusion Detection

--Intrusion detection in unmanned-aerial-vehicle (UA V) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning (QML) approaches--quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT -NNs)--against strong classical baselines. All models consume an 8-feature flow representation and are evaluated under identical preprocessing, balancing, and noise-model assumptions. Results reveal clear trade-offs: quantum kernels and QT -NNs excel in low-data, nonlinear regimes, while deeper QNNs suffer from trainability issues, and CNNs dominate when abundant data offset their larger parameter count. The complete codebase and dataset partitions are publicly released to enable reproducible QML research in network security.