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arXiv · 2512.01365

Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection

Swati Kumari·Shiva Raj Pokhrel·Swathi Chandrasekhar·Navneet Singh·Hridoy Sankar Dutta·Adnan Anwar·Sutharshan Rajasegarar·Robin Doss
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Abstract

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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Swati Kumari, Shiva Raj Pokhrel, Swathi Chandrasekhar, Navneet Singh, Hridoy Sankar Dutta, Adnan Anwar, Sutharshan Rajasegarar, Robin Doss. 2025-12-01. Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection. https://arxiv.org/abs/2512.01365

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