物联网异常检测的量子自编码器可训练核建模
Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
AI总结：
本文提出量子自编码器与量子支持向量分类结合的物联网异常检测方法，展示了在当前NISQ设备上的量子优势和实际应用潜力。
摘要：
随着网络威胁的加剧和物联网流量的高维复杂性，传统异常检测方法已无法应对。尽管深度学习提供了改进，但计算瓶颈限制了大规模实时部署。我们提出了一种量子自编码器（QAE）框架，将网络流量压缩为判别性潜在表示，并利用量子支持向量分类（QSVC）进行入侵检测。在三个数据集上评估，我们的方法在理想模拟器和IBM量子硬件上均实现了更高的准确性，展示了当前NISQ设备上的实际量子优势。关键在于适度的去极化噪声起到了隐含的正则化作用，稳定了训练并增强了泛化能力。本工作确立了量子机器学习作为现实世界网络安全挑战的可行、硬件就绪的解决方案。
英文摘要：
Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
