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.

翻译：基于无监督异常检测的入侵检测需要模型能够泛化到训练过程中未观测到的攻击模式。本研究首次大规模评估了混合量子经典自编码器在此任务中的表现。我们构建了一个统一的实验框架，系统迭代了关键量子设计选择，包括量子层位置、测量方法、变分与非变分公式以及潜在空间正则化。对三个基准NIDS数据集的实验表明，在最优配置下，混合量子经典自编码器的性能可匹配或超越经典模型，但架构决策对其影响更为显著。在零日攻击评估中，配置良好的混合量子经典模型比经典和监督基线表现出更强且更稳定的泛化能力。模拟门噪声实验揭示了早期性能退化，表明需要噪声感知的混合量子经典设计。这些成果首次通过数据驱动方式刻画了混合量子经典自编码器在网络入侵检测中的行为，并指出了决定其实用可行性的关键因素。所有实验代码与配置均可在 https://github.com/arasyi/hqcae-network-intrusion-detection 获取。