Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection

TL;DR

This paper introduces a hybrid quantum-classical framework combining wavelet transforms and quantum support vector machines to improve network intrusion detection accuracy in noisy IoT environments.

Contribution

It presents a novel integration of Quantum Haar Wavelet Packet Transform with QSVM, optimized with SPSA, for enhanced anomaly classification in cybersecurity.

Findings

Achieved 96.67% accuracy on BoT-IoT dataset.

Achieved 89.67% accuracy on IoT-23 dataset.

Outperformed quantum autoencoder methods by over 7%.

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.