Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection

• URL: http://arxiv.org/abs/2512.01365v1
• Date: Mon, 01 Dec 2025 07:23:20 GMT
• Title: Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
• Authors: Swati Kumari, Shiva Raj Pokhrel, Swathi Chandrasekhar, Navneet Singh, Hridoy Sankar Dutta, Adnan Anwar, Sutharshan Rajasegarar, Robin Doss,
• Abstract summary: We present a novel quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT)<n>Our methodology employs amplitude-encoded quan-tum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing.<n> 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.
• Score: 9.036718544324573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network traffic anomaly detection is a critical cy- bersecurity 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 quan- tum 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 train- ing 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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