Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
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.
Cite
@article{arxiv.2512.01365,
title = {Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection},
author = {Swati Kumari and Shiva Raj Pokhrel and Swathi Chandrasekhar and Navneet Singh and Hridoy Sankar Dutta and Adnan Anwar and Sutharshan Rajasegarar and Robin Doss},
journal= {arXiv preprint arXiv:2512.01365},
year = {2025}
}