@Article{cmc.2026.075326,
AUTHOR = {Wajdan Al Malwi, Fatima Asiri, Muhammad Shahbaz Khan},
TITLE = {Quantum–Enhanced Intrusion Detection Using Quantum Circuit Born Machines for Zero-Day Attack Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26394},
ISSN = {1546-2226},
ABSTRACT = {Modern intrusion detection systems (IDS) struggle to recognise zero-day cyberattacks, as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour. This work presents a hybrid quantum–classical intrusion detection framework in which a Quantum Circuit Born Machine (QCBM) models benign traffic as a probabilistic quantum state. The trained QCBM assigns each network flow a <i>Quantum Anomaly Score</i> (QAS), defined as the negative log-likelihood under the learned benign distribution, which is subsequently fused with classical flow statistics in a Light Gradient Boosted Machine (LightGBM) classifier. The proposed system employs a 16-qubit, three-layer QCBM (approximately 192 quantum gates) trained using up to <mml:math id="mml-ieqn-1"><mml:msup><mml:mn>10</mml:mn><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msup></mml:math> measurement shots on the CICIDS2017 dataset. Experimental results show that integrating the QAS does not degrade supervised detection performance on known attacks (Accuracy <mml:math id="mml-ieqn-2"><mml:mo>≈</mml:mo><mml:mn>0.996</mml:mn></mml:math>, Receiver Operating Characteristic-Area Under Curve (ROC-AUC) <mml:math id="mml-ieqn-3"><mml:mo>≈</mml:mo><mml:mn>0.9995</mml:mn></mml:math>), while providing an additional anomaly-sensitive signal under strict zero-day conditions. When entire attack families are withheld during training, the QAS assigns systematically higher anomaly scores to unseen attacks than to benign traffic and achieves unsupervised zero-day ROC-AUC values of approximately 0.78 across multiple attack types. These findings demonstrate that shallow, resource-efficient quantum generative models can act as interpretable probabilistic priors for benign behaviour, complementing classical IDS pipelines and enabling principled anomaly awareness under realistic Noisy Intermediate-Scale Quantum (NISQ) constraints.},
DOI = {10.32604/cmc.2026.075326}
}