Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
This addresses cybersecurity challenges in IoT by demonstrating a practical quantum advantage on current NISQ devices.
The authors tackled IoT anomaly detection by developing a quantum autoencoder framework that compresses network traffic and uses quantum support vector classification, achieving improved accuracy on simulators and IBM Quantum hardware with moderate noise acting as regularization.
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.