量子经典混合自编码器可有效检测未知网络攻击，性能优于传统方法。
Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection
- 采用量子-经典混合架构，优化量子层位置与参数训练方式。
- 在三个基准数据集上达到或超越经典模型性能，零日攻击下更稳定。
- 适合对网络安全、量子机器学习感兴趣的科研与工程人员。
无监督异常检测需要模型能泛化到训练中未见的攻击模式。本文首次对混合量子-经典（HQC）自编码器在此任务上的表现进行了大规模评估。构建统一实验框架，系统测试了量子层位置、测量方式、变分与非变分形式及潜在空间正则化等关键设计选择。在三个基准网络入侵检测数据集上的实验表明，最优配置的HQC自编码器性能可媲美甚至超过经典模型，但对架构选择更敏感。在零日攻击评估中，配置良好的HQC模型展现出更强且更稳定的泛化能力，优于经典与有监督基线。模拟门噪声实验显示早期性能下降，表明需发展抗噪的HQC设计。这些结果首次提供了针对网络入侵检测的HQC自编码器行为的数据驱动分析，并明确了其实际可用性的关键因素。所有实验代码与配置已公开于https://github.com/arasyi/hqcae-network-intrusion-detection。
原文摘要 · Abstract (English)
Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need for noise-aware HQC designs. These results provide the first data-driven characterization of HQC autoencoder behavior for network intrusion detection and outline key factors that govern their practical viability. All experiment code and configurations are available at https://github.com/arasyi/hqcae-network-intrusion-detection.
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