Both VQC and QKA achieved competitive intrusion detection performance on the UNSW-NB15 dataset using identical preprocessing and feature encoding. Quantum Kernel Alignment demonstrated more stable optimization by reducing the number of trainable parameters while preserving classification accuracy. Fast QKA significantly reduced computational time compared with conventional scaled QKA, making it more suitable for practical hybrid quantum–classical intrusion detection systems. GPU-accelerated Qiskit Aer simulation substantially reduced execution time, enabling efficient experimentation using eight-qubit quantum circuits. The experimental results confirm that trainable quantum kernel methods constitute a promising alternative to variational quantum classifiers for near-term quantum intrusion detection, particularly under current NISQ hardware limitations.
Both VQC and QKA achieved competitive intrusion detection performance on the UNSW-NB15 dataset using identical preprocessing and feature encoding. Quantum Kernel Alignment demonstrated more stable optimization by reducing the number of trainable parameters while preserving classification accuracy. Fast QKA significantly reduced computational time compared with conventional scaled QKA, making it more suitable for practical hybrid quantum–classical intrusion detection systems. GPU-accelerated Qiskit Aer simulation substantially reduced execution time, enabling efficient experimentation using eight-qubit quantum circuits. The experimental results confirm that trainable quantum kernel methods constitute a promising alternative to variational quantum classifiers for near-term quantum intrusion detection, particularly under current NISQ hardware limitations.
This software and experiment package provides the reproducibility materials for a comparative study of Variational Quantum Classification (VQC) and Quantum Kernel Alignment (QKA) for binary network intrusion detection using the UNSW-NB15 benchmark dataset. Version 2 preserves the original VQC/QKA/Fast-QKA experiment material and adds a reviewer-requested validation layer consisting of a paired 10-seed comparison of RBF-SVC, VQC, and QKA under identical sampling and train-only preprocessing. Each seed uses 200 balanced training and 200 balanced testing observations, eight PCA components, and an eight-qubit ZZFeatureMap representation. The package includes per-seed metrics, summary statistics, confusion counts, runtime measurements, optimizer outputs, objective histories, environment manifests, and paired statistical tests. Across the ten seeds, RBF-SVC achieved the highest mean accuracy (0.8535 ± 0.0282), followed by QKA (0.8275 ± 0.0290) and VQC (0.6050 ± 0.0428). QKA achieved a mean ROC-AUC of 0.9162 ± 0.0260 and substantially outperformed the tested VQC, while the classical baseline remained strongest overall. Version 2 also includes the final QKA finite-shot and synthetic-noise sensitivity experiment implemented with Qiskit Aer using 1,024 shots and simulator seeds 101, 202, and 303. Under the highest tested noise setting (p1 = 0.005, p2 = 0.030, readout error = 0.030), QKA ROC-AUC decreased from 0.9175 in the exact reference to 0.5875 ± 0.0217. The package contains notebooks, raw and summarised results, kernel matrices, publication figures, documentation, provenance records, software requirements, metadata templates, manifests, and SHA-256 checksums. Historical Fast-QKA results are retained for provenance but are not used as part of the repeated-seed inferential comparison. The experiments are simulation-based and do not establish general quantum advantage or physical-device readiness.
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.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Quantum Neural Network (QNN) provides an exciting alternative to machine learning as they take advantage of quantum computer properties when making decisions and processing data. However, the integration of high-dimensional classical data with the limited number of qubits available on current Noisy Intermediate-Scale Quantum computers presents a major difficulty. This paper presents a complete hybrid quantum-classical architecture in Qiskit that utilizes the Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD) dataset to classify network intrusions among multiple classes. In order to work around the exponential cost of computations and reduce the number of times quantum circuits need to be run due to hardware limitations, a subset of the dataset will be created to use during both training and testing. The architecture proposed in this paper uses a four-qubit Variational Quantum Circuit (VQC). First, a classical feature vector is used to create a quantum Hilbert space via a ZZFeatureMap, which initializes the qubits via a Hadamard transformation followed by phase rotation gates that create controlled-entanglement between pairs of features. The quantum states are then passed through a RealAmplitudes variational circuit, while the classical layer optimizes the parameters using the Constrained Optimization BY Linear Approximations (COBYLA) algorithm. The overall accuracy of the model when tested on five classes of intrusion detection results in an accuracy level of 59%, with very strong classification accuracies for both Denial of Service (DoS) and User-to-Root (U2R) attacks as demonstrated by their F1 scores. The proposed QNN achieves a highly competitive baseline of 86% accuracy and 86% recall in specific configurations. These results underscore the potential of shallow, lower-complexity QNN architectures as an emergent tool for cybersecurity in the quantum era.
Weaknesses arising from heterogeneous device structure, high-dimensional traffic patterns, and more complex nonlinear attack behaviors. The traditional intrusion detection systems (IDS), which rely on the classical machine learning models, often face the challenges of scalability, feature explosion, and low representational ability when handling large-scale IoT traffic data. To meet these challenges, we propose a Hybrid Quantum-Classical Variational Neural Network (HQCVNN) for effective intrusion detection in IoT systems. The model utilizes a Variational Quantum Circuit (VQC) as a nonlinear quantum feature map followed by dense neural layers for overall classification. Angle embedding with entanglement layers enables the encoding of quantum data and leads to a better separability of features in high-dimensional space, whilst the classical optimization guarantees efficient training on NISQ time scales. The model is implemented using Qiskit and evaluated on classical benchmark intrusion detection datasets. Experimental results show the proposed method has better classification accuracy, F1-score, and complexity reduction than conventional ANNs and SVMs. The results demonstrate the promise of hybrid quantum machine learning systems with end-to-end general-purpose capabilities in future IoT security and provide pragmatic impressions on how to deploy them on near-term quantum processors.
ABSTRACT The amount of cyber threats is growing at an exponential rate, and intelligent network intrusion detection systems (NIDS) that can cope with high dimensional and complex network data, is essential. Traditional machine‐learning (ML) approaches might not be as effective to identify complex hidden correlations between traffic attributes and at identifying advanced and unseen attacks. In this paper we suggest a classical–quantum hybrid kernel framework that enhances network intrusion detection. Using principal component analysis (PCA), network traffic features are normalized and dimensionality reduced to be compatible with noisy intermediate‐scale quantum (NISQ) devices, whilst preserving over 95% of the variance in the data. The classification is achieved by a fidelity‐based quantum kernel with a classical support vector machine(SVM). The proposed hybrid quantum kernel support vector machine (QK‐SVM)is tested using two datasets, CIC‐IDS2017 and CICIoT2023. Experimental results, achieving accuracy up to 98.1%, F1‐score up to 0.98, and anomaly detection rate of 97.6% with linear and RBF‐SVM baselines, are comparable to that of Random Forest. The results are based on the Qiskit Aer simulator and do not reflect the performance on actual quantum devices.