Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914207913476096 |
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| author | Kumari, Swati Pokhrel, Shiva Raj Chandrasekhar, Swathi Singh, Navneet Dutta, Hridoy Sankar Anwar, Adnan Rajasegarar, Sutharshan Doss, Robin |
| author_facet | Kumari, Swati Pokhrel, Shiva Raj Chandrasekhar, Swathi Singh, Navneet Dutta, Hridoy Sankar Anwar, Adnan Rajasegarar, Sutharshan Doss, Robin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection Kumari, Swati Pokhrel, Shiva Raj Chandrasekhar, Swathi Singh, Navneet Dutta, Hridoy Sankar Anwar, Adnan Rajasegarar, Sutharshan Doss, Robin Quantum Physics Machine Learning 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. |
| title | Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2512.01365 |