Benchmarking Classical and Quantum Machine Learning for Intrusion Detection Across Multiple Datasets

Hybrid Quantum-Classical Intrusion Detection with Quantum Feature Representations under NISQ Constraints

Intrusion detection systems must balance predictive quality, robustness, and computational cost, yet the role of quantum representations under NISQ constraints remains unclear. This paper investigates whether quantum principal component analysis (QPCA) can provide useful features for IDS without relying on claims of end-to-end quantum superiority. We evaluate PCA- and QPCA-based pipelines combined with Logistic Regression, SVM, and Random Forest, and include a QPCA→VQC branch as a comparative quantum arm. Experiments on CICIDS2017 and NSL-KDD under nisq preset and scaled preset use simulator-based quantum execution, multi-seed evaluation, Wilcoxon–Holm tests, bootstrap confidence intervals, and cost analysis. Results show no universal advantage of QPCA, but selective ranking gains: ROC-AUC improves from 0.5397 to 0.8772 (CICIDS2017) and from 0.7453 to 0.8063 (NSL-KDD), both with corrected significance under matched data budgets. Overall, QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.

Hybrid Quantum-Inspired Machine Learning for Financial Fraud Detection and Rare Event Identification

Classical ML models for fraud detection tend to break down on rare events, which are the cases that matter most. In imbalanced transaction datasets, new fraud patterns often fall outside what these models were trained to recognize. This paper presents a Hybrid Quantum-Classical Anomaly Detection Framework built to address that gap. Particle Swarm Optimization (PSO) handles feature selection, after which transaction features are encoded into quantum-inspired Hilbert spaces using Angle and Amplitude Encoding. Two quantum models, a Quantum Kernel SVM (QK-SVM) and a Variational Quantum Circuit (VQC), process these representations, and their outputs are fused with scores from Random Forest, XGBoost, and One-Class SVM through a weighted scoring scheme. On the Credit Card Fraud Detection benchmark, the hybrid framework outperforms classical-only baselines on rare event detection across Precision, Recall, F1-Score, and ROC-AUC, without needing actual quantum hardware.

Assessing the efficacy of quantum ML: QSVM and QNN on IBM simulators

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

A quantum-attribution audit is introduced that quantifies how much of any gain is genuinely attributable to the quantum component of quantum models, and attributes this to classical preprocessing and regularisation rather than quantum effects.

Quantum Kernel Concentration Under Class Imbalance: Empirical Characterisation with Discrimination Ratio and Quantum Imbalance Vulnerability Score

Quantum kernel methods are a candidate approach for machine learning on near-term quantum hardware, but two practical problems limit their deployment: kernel values concentrate exponentially as the qubit count grows, and real-world datasets are often severely class-imbalanced. We present the first systematic empirical study of how these two effects interact. We define two diagnostic metrics, the Discrimination Ratio (DR) and the Quantum Imbalance Vulnerability Score (QIVS), which measure whether quantum kernels retain minority-class separability under concentration. Experiments span ten log-spaced imbalance ratios, five random seeds, five qubit counts (4 to 12), and five real-world datasets, and yield three results. First, DR stays above 1.0 at every qubit count tested (4 to 12), so the discriminative signal survives concentration. Second, at extreme imbalance (IR below 0.003), quantum kernel SVMs retain positive discriminative signal, crossing above DR=1.0 by IR≈0.0028, while the classical oversampling methods SMOTE and ADASYN produce zero minority-class recall throughout the same regime, a practical advantage for quantum kernels at the imbalance extreme. Third, QIVS follows a broadly monotonic decreasing trend, falling from 13.25 to 5.54 as the imbalance ratio increases, with a single minor fluctuation smaller than the cross-seed variability we measure elsewhere in the sweep. This trend makes QIVS a reliable diagnostic for practitioners choosing quantum kernels on imbalanced tasks.

Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do

Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them. We benchmark fidelity-kernel SVMs and variational classifiers against six tuned classical models on public power-system attack data (Mississippi State/ORNL), across white-box, transfer, decision-based black-box, and poisoning attacks. Our headline finding is methodological: the benchmark's answers are set by the evaluator's choices before the models. Eight choices -- six in the evaluation protocol, two in the tuning the benchmark itself runs -- each reversed or moved a conclusion at fixed models. The largest is the split: the row-level protocol scores 0.905 macro-F1 where holding whole source files out leaves 0.594, and in the capped matched-dimensionality regime the quantum arm sits within noise of chance with the classical arm 0.024 above it. A fidelity kernel looks most robust until attacked directly (retention 0.886 to 0.064); a mis-fitted surrogate manufactures a 10x asymmetry; an unseeded black-box attack moves 75% between restarts. A positive control explains the accuracy null: the labels, not the pipeline. We give the control that catches each choice and release the seeded benchmark.