ANHQ-IDS: A Minimax-Based Adaptive Noise-Aware Hybrid Quantum Framework with Resource Constrained Feature Selection for Network Intrusion Detection

Abstract

Network intrusion detection systems (NIDS) must classify high-dimensional, heavily imbalanced traffic flows under tight operational constraints, and the flow features produced by modern capture tools routinely exceed the qubit budgets available on today’s noisy intermediate-scale quantum (NISQ) hardware. Existing quantum machine-learning (QML) detectors typically either assume that every extracted feature can be encoded on a dedicated qubit or apply generic dimensionality reduction that is blind to the downstream quantum encoder; in both cases the qubit budget — the binding constraint on real devices — never drives which features are encoded, and evaluation is frequently confined to ideal statevector simulation that omits shot noise, depolarizing error, and readout confusion. This paper presents ANHQ-IDS, an adaptive, noise aware hybrid quantum intrusion-detection framework that makes the qubit budget a first-class design constraint. A resource-constrained feature-selection stage scores candidate features through a composite objective jointly balancing mutual-information relevance, inter-feature redundancy, cross-split stability, and an explicit quantum-encoding cost, and greedily selects a subset sized to a target qubit budget rather than fixed a priori. The retained features are embedded through parameterized angle and second-order (ZZ) feature maps and classified by a hardware-efficient variational quantum classifier (VQC) trained with gradient-free optimizers (COBYLA and SPSA) under finite-shot sampling. To characterize behaviour under realistic hardware conditions, the classifier is evaluated on a statevector reference and under synthetic depolarizing and readout-error models, and benchmarked on CIC-IDS2017 against classical baselines (logistic regression, random forest, support vector machine) using a leakage-resistant train/validation/test protocol. On a class-balanced CIC-IDS2017 benchmark, the ZZ-encoded VQC attains macro-F1 0.766 and ROC-AUC 0.853 under exact simulation — exceeding logistic regression (0.502) and approaching a support-vector baseline, while a random forest remains strongest (0.964); performance degrades gracefully under depolarizing and readout noise, consistent with the predicted (1−p) contraction. All preprocessing, feature-selection, and quantum-training components are released as a reproducible Qiskit package with fixed seeds and recorded circuit-resource metadata (qubits, depth, gate and parameter counts).