用量子小波变换提升量子支持向量机的网络入侵检测能力
Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
- 结合量子小波包变换与优化量子核的量子支持向量机
- 在无噪和去极化噪声下准确率分别达96.67%和89.67%
- 适合关注量子机器学习在网络安全中应用的研究者
网络流量异常检测是物联网环境下的关键安全挑战。本文提出一种新型混合量子-经典框架，将改进的量子支持向量机（QSVM）与量子哈尔小波包变换（QWPT）结合，以在真实噪声中间尺度量子条件下实现优异的异常分类性能。方法采用振幅编码的量子态制备、多层级QWPT特征提取，并通过香农熵分析与卡方检验进行行为分析。特征使用基于保真度的量子核优化的QSVM进行分类，优化过程采用同时扰动随机逼近（SPSA）的混合训练策略。在无噪和去极化噪声条件下评估显示：在BoT-IoT数据集上准确率达96.67%，在IoT-23数据集上为89.67%，优于量子自编码器方法超过7个百分点。
原文摘要 · Abstract (English)
Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.
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