Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need for noise-aware HQC designs. These results provide the first data-driven characterization of HQC autoencoder behavior for network intrusion detection and outline key factors that govern their practical viability. All experiment code and configurations are available at https://github.com/arasyi/hqcae-network-intrusion-detection.
Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need for noise-aware HQC designs. These results provide the first data-driven characterization of HQC autoencoder behavior for network intrusion detection and outline key factors that govern their practical viability. All experiment code and configurations are available at https://github.com/arasyi/hqcae-network-intrusion-detection.
Intrusion Detection Systems (IDSs) are a cornerstone of modern cybersecurity; however, their performance is increasingly constrained by high-dimensional network traffic and the scalability limitations of classical machine learning approaches. Quantum Machine Learning (QML) has recently emerged as a promising paradigm by leveraging quantum feature spaces and hybrid quantum–classical optimization. In this work, we present a unified and reproducible benchmarking framework for QML-based intrusion detection under realistic Noisy Intermediate-Scale Quantum (NISQ) constraints. The proposed framework integrates hybrid Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs) within a consistent experimental pipeline, enabling a systematic comparison of quantum models, embedding strategies, and dimensionality reduction techniques. Experiments are conducted on the NSL-KDD benchmark dataset for both binary (normal versus attack) and multiclass intrusion detection tasks. Multiple quantum embedding strategies, including angle, amplitude, and Instantaneous Quantum Polynomial (IQP) embeddings, are evaluated in conjunction with PCA-based dimensionality reduction to analyze trade-offs between expressivity, circuit complexity, and computational cost. The proposed models are benchmarked against strong classical baselines under identical preprocessing and evaluation conditions. The results demonstrate that QNN- and QSVM-based IDS models achieve competitive and, in several scenarios, superior performance compared to classical approaches, particularly in multiclass settings characterized by nonlinear and imbalanced data distributions. In addition, we provide an in-depth analysis of scalability limitations, training complexity, and NISQ feasibility, highlighting practical considerations for real-world deployment. To ensure full reproducibility, all implementation details, datasets, and experimental configurations are publicly available at: https://github.com/raidanis/QML-IDS-NSLKDD. These findings position hybrid QML as a promising complementary paradigm for next-generation intrusion detection systems, particularly in low-dimensional and high-complexity feature spaces.
Quantum machine learning (QML) is increasingly investigated for network intrusion detection, but the practical value of current quantum models remains uncertain because many studies rely on small simulated data sets, idealized execution, weak classical baselines, or isolated accuracy comparisons. This study develops and evaluates a resource-aware hybrid quantum-classical intrusion detection framework that combines fixed fidelity quantum kernels, repaired Quantum Kernel Alignment (QKA), a Variational Quantum Classifier (VQC), LightGBM, prototype-based kernel reduction, and probabilistic stacking. Experiments use the UNSW-NB15 benchmark under a leakage-aware preprocessing protocol and repeated evaluation across five random seeds. The experimental program covers 4- and 6-qubit representations, multiple training sizes, prototype budgets, and finite-shot configurations, yielding 260 experimental configurations/runs. The strongest quantum-assisted model, a 6-qubit QKA-VQC-LightGBM stack at 100 samples per class, obtains mean F1=0.8480, MCC=0.7092, ROC-AUC=0.9398, and PR-AUC=0.9302. Repaired QKA improves F1 from 0.8043 for the corresponding fixed kernel to 0.8089, while 50-prototype QKA attains F1=0.8257 with a more favorable kernel-workload trade-off than larger prototype budgets. Finite-shot experiments between 1,024 and 4,096 shots remain stable in simulation. However, full-data LightGBM remains stronger overall (F1=0.8957, MCC=0.8068, ROC-AUC=0.9755, PR-AUC=0.9704), and corrected statistical tests do not establish general quantum superiority. These findings motivate a Selective Quantum Intrusion Detection System (S-QIDS), in which classical inference handles high-confidence traffic and quantum processing is reserved for uncertain, novel, or high-risk observations. The study therefore reframes near-term quantum intrusion detection from unconditional quantum advantage toward measurable quantum utility, quantum-classical complementarity, and resource-aware quantum intervention.
Intrusion detection remains a critical challenge in cybersecurity, particularly when dealing with high-dimensional data. In this work, we propose a hybrid quantum machine learning model that integrates a Quantum Autoencoder (QAE) for dimensionality reduction with a Quantum Neural Network (QNN) classifier. The QAE compresses input features into latent states without relying on classical methods such as Principal Component Analysis (PCA), preserving quantum-relevant information. These latent states are then classified using a Variational Quantum Circuit implemented with Qiskit. We systematically evaluated different ansatz architectures and hyperparameters on the NSL-KDD dataset. The best configuration, an EstimatorQNN with EfficientSU2 ansatz (4 repetitions), achieved an F1-score of 0.767, with balanced recall (0.70) and precision (0.84). These results highlight the potential of quantum-enhanced models for addressing the scalability challenges of intrusion detection, representing a step toward applying High-Performance Computing principles to cybersecurity.
The practical value of quantum machine learning for network anomaly detection remains unclear under controlled and comparable benchmarking conditions. This study evaluates a trash-qubit-based Quantum Autoencoder (QAE) against seven classical anomaly-detection methods on UNSW-NB15 using shared data partitions, preprocessing steps, model selection procedures, and threshold calibration protocols. The experimental design included five independent seeds, three training set sizes, two feature budgets, and 12 QAE architectures, together with low-FPR analysis, finite-shot and synthetic-noise evaluation, less-constrained classical reference settings, and a cross-dataset assessment on TON_IoT. The Angle-RY encoding with a Real-Amplitudes Ring ansatz at depth L = 2 achieved the highest mean validation-selection AUPRC. On UNSW-NB15, the QAE was competitive in AUPRC and AUROC but remained behind the strongest classical baselines in F1-score, and its attack recall declined sharply under low-FPR constraints. The TON_IoT results further showed that relative model performance depended on the dataset. Under finite-shot and synthetic-noise conditions, anomaly-score rankings were largely preserved, whereas threshold-based decisions were more sensitive. Overall, the QAE showed competitive continuous-score discrimination on UNSW-NB15, but achieving reliable detection at low false-alarm rates remains a key operational challenge.
Variational quantum circuits are said to buy richer decision boundaries for fewer parameters than a classical network needs, and intrusion detection on network flows is among the tasks where that claim is made most often. What backs it up is thinner than the publication count suggests: hybrid quantum IDS papers typically show one training run against loosely specified baselines, leaving no way to separate an architectural effect from run-to-run variance. We report a controlled evaluation of a hybrid quantum- classical detector, Q-NIDS, across three configurations sharing one preprocessing pipeline. Two use CICIDS2017 with a six-qubit circuit combining data re-uploading, a learnable entanglement topology and quantum recurrent memory gates; the third scales to eight qubits on BigFlow-NIDS, a 66.9-million-flow NetFlow corpus, using a batched state-vector simulator matching PennyLane's lightning.qubit to 9.7 × 10-8. On CICIDS2017 the calibrated model reaches 87.97% accuracy and 49.09% macro F1, exceeding a 1D-CNN (40.52 ± 0.83%), an LSTM (39.06 ± 2.04%) and an MLP (37.98 ± 3.97%) trained on identical data, but falling 12.47 points short of a Random Forest (61.56 ± 0.20%). Three findings cut against the usual story. Most of the margin over the neural baselines is bought by post-hoc prior adjustment, a classical correction worth 8.11 macro-F1 points. The learnable entanglement topology never trains: a non-differentiable branch in gate selection cuts its gradient path, after which an unopposed entropy regulariser drives the adjacency matrix to a value where every conditional gate switches off. On BigFlow-NIDS an ablated control with the quantum layer deleted reaches 10.79% macro F1 against the full model's 12.53%, a margin too narrow to credit to quantum processing. We report all of it, failed configurations included, and argue that controls of this kind belong in any hybrid quantum machine learning claim.
The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.