Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection
For network security practitioners, this work offers a practical strategy to enhance the reliability of quantum machine learning-based intrusion detection systems, though improvements are incremental and dataset-dependent.
The paper proposes a hybrid quantum-classical ensemble framework (MQE) that fuses Quantum SVM and Quantum Neural Network outputs via a Random Forest meta-learner for network intrusion detection. On TON IoT and CICIDS2017 datasets, MQE improves detection performance and reliability over standalone quantum models, with gains varying by dataset and metric.
Intrusion Detection Systems (IDSs) must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether heterogeneous quantum learners can provide useful and non-redundant decision information for IDS tasks. We study Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs), which rely on different learning mechanisms and exhibit distinct prediction behaviors. To combine these models, we propose the System-Level Meta-Quantum Ensemble (MQE), a hybrid quantum-classical framework that fuses QSVM and QNN outputs using a Random Forest meta-learner. The meta-learner captures agreement and disagreement patterns between the quantum branches to improve prediction stability and detection performance. Experiments on TON IoT and CICIDS2017 show that MQE improves selected performance, low-FPR, and reliability metrics over several standalone quantum learners, with gains depending on the dataset, metric, and fusion representation. The results highlight meta-level fusion as a practical strategy for building more reliable QML-based IDS pipelines.