用量子机器学习提升无人机蜂群入侵检测能力
Quantum Machine Learning for UAV Swarm Intrusion Detection
- 对比三种量子机器学习方法在流量特征上的表现
- 量子核与量子训练神经网络在数据少时更优
- 适合关注量子计算与网络安全交叉研究者
无人机蜂群入侵检测面临高移动性、非平稳流量和严重类别不平衡的挑战。基于包含五种攻击类型的12万条流数据的仿真语料库，我们对三种量子机器学习（QML）方法——量子核、变分量子神经网络（QNNs）和混合量子训练神经网络（QT-NNs）——与强基准经典模型进行对比评估。所有模型均采用8维流特征表示，并在相同预处理、样本平衡及噪声建模条件下测试。分析了编码策略、电路深度、量子比特数和采样噪声的影响，评估指标包括准确率、宏平均F1、ROC-AUC、马修斯相关系数及量子资源开销。结果表明：量子核与QT-NN在小数据、非线性场景下表现优异；更深的QNN因可训练性问题性能下降；当数据充足时，经典卷积神经网络仍占优势。完整代码库与数据划分已公开，以支持网络安全部门的可复现量子机器学习研究。
原文摘要 · Abstract (English)
Intrusion detection in unmanned-aerial-vehicle (UAV) 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. We analyse the influence of encoding strategy, circuit depth, qubit count, and shot noise, reporting accuracy, macro-F1, ROC-AUC, Matthews correlation, and quantum-resource footprints. 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.
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