Evaluation of Quantum Machine Learning Models for Network Anomaly Detection

Swathi Chandrasekhar; Shiva Raj Pokhrel

doi:10.1109/icoin68469.2026.11480629

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arXiv · 2611.09253

Evaluation of Quantum Machine Learning Models for Network Anomaly Detection

Swathi Chandrasekhar·Shiva Raj Pokhrel

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Abstract

The proliferation of Internet-connected devices has intensified the risk of large-scale network intrusions, necessitating more advanced detection methodologies for the post quantum era. This work presents, to the best of our knowledge, a com- prehensive investigation of quantum machine learning (QML) for anomaly detection on the BoT-IoT dataset, a widely adopted benchmark for IoT security. Leveraging feature-engineered rep- resentations, we implement and evaluate four representative quantum classifiers: Quantum Support Vector Classification (QSVC), Quantum Neural Networks (QNN), and Variational Quantum Classifiers (VQC).

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BibTeX RIS

Swathi Chandrasekhar, Shiva Raj Pokhrel. 2026-11-06. Evaluation of Quantum Machine Learning Models for Network Anomaly Detection. https://doi.org/10.1109/icoin68469.2026.11480629

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