Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection

2026-05-26

AI Summary

This study addresses the challenge of achieving high sensitivity while maintaining a low false positive rate in network intrusion detection under class-imbalanced and heterogeneous IoT traffic. To this end, the authors propose a Mixed Quantum-Classical Ensemble framework (MQE), which, for the first time, integrates complementary quantum support vector machines (QSVM) and quantum neural networks (QNN) at the meta-level. A random forest meta-learner is employed to model both the consensus and divergence among their predictions, thereby enhancing the robustness, stability, and reliability of detection. Experimental results on the TON IoT and CICIDS2017 datasets demonstrate that MQE significantly outperforms individual quantum models across key metrics, including false positive rate, detection performance, and overall reliability.

Abstract

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.

Problem

• Network Intrusion Detection
• Class Imbalance
• False Positive Rate
• Heterogeneous IoT Traffic
• Quantum Machine Learning

Innovation

• Meta-Quantum Ensemble
• Quantum Machine Learning
• Network Intrusion Detection
• Heterogeneous Quantum Learners
• Low False Positive Rate