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.
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.
Machine learning Intrusion detection systems (IDS) fail with large feature spaces and imbalanced attack data. At the same time, current quantum devices are limited by noise and small qubit counts. In this work, we explore a simple hybrid approach in which quantum circuits are used only for feature transformation, while a classical model performs the final classification. Normalized NSL-KDD traffic records are first reduced by principal component analysis (PCA), then encoded into quantum states and processed by a low-depth variational quantum circuit (VQC). The measured outputs form a compact feature vector that is fed to a support vector machine (SVM). On the NSL-KDD dataset, the proposed VQC+SVM model reaches 80.9% accuracy and an F1-score of 0.814, which is higher than PCA+SVM (73.0%) and autoencoder+SVM (75.7%). An ablation study over different circuit depths and qubit counts shows that a 4-qubit, 3-layer circuit provides the most stable performance under NISQ-like conditions.
With cyber-attacks becoming increasingly sophisticated, modern network intrusion detection systems (NIDSs) are relying on machine learning (ML) methods for their flexibility in detecting subtle anomalous patterns in huge amounts of network data. However, classical ML methods such as support vector machines (SVMs) often rely on the conversion of low-dimensional data into a high-dimensional space, creating complex linear systems that are time-consuming to evaluate on large data inputs such as network flow logs. We propose addressing this limitation by employing a hybrid quantum-classical ML model to leverage quantum computing's (QC's) superiority in high-dimensional areas. We constructed a quantum kernel with an SVM model and evaluated it on four different network attacks from a modern intrusion detection dataset. Results reveal an average hardware accuracy rate of 85% with noticeably small deviations between runs, suggesting that quantum kernels may be a noise-resistant solution. We evaluated these results alongside classical and noiseless quantum simulator benchmarks.
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.
Intrusion Detection Systems (IDS) rely on intelligent learning techniques to identify complex and evolving attack patterns within network traffic. This paper presents a comparative study of Support Vector Machines (SVM), Quantum Support Vector Machines (QSVM), and Variational Quantum Classifiers (VQC) in the context of intrusion detection. A unified evaluation framework is employed to compare these models based on feature representation, computational complexity, scalability, hardware requirements, and deployment feasibility. The analysis examines how QSVM and VQC utilize quantum feature mapping and parameterized quantum circuits to improve data representation capabilities. Nevertheless, their practical adoption remains limited by the constraints of current Noisy Intermediate-Scale Quantum (NISQ) devices, including hardware noise, restricted qubit resources, and scalability limitations. Conversely, SVM continues to be a dependable and scalable solution for present-day intrusion detection systems. The study further identifies the absence of a standardized framework for evaluating classical and quantum approaches as an important research gap. Overall, the findings suggest that hybrid quantum–classical learning frameworks provide the most realistic path toward adopting quantum machine learning for future cybersecurity solutions
The application and assessment of quantum-based anomaly detection models in cybersecurity are examined in this work. The efficacy of quantum machine learning approaches for detecting several cyber threats, including Denial of Service (DoS), Remote-to-Local (R2L), and botnet penetration, using two well-known intrusion detection datasets: NSL-KDD and CICIDS2017. Angle and amplitude encoding techniques are worn to preprocess and instruct traditional data into quantum-readable formats, making it compatible with quantum circuits. Due to the present constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, the models are developed using Qiskit, using parameterized quantum layers via the TwoLocal ansatz, and simulated on classical hardware using Qiskit Aer. Metrics are used to compare performance to traditional machine learning models, such as Support Vector Machines (SVM), Random Forests (RF), and Multilayer Perceptions (MLP). The quantum SVM (QSVM) performs better in classification on both datasets, according to the results, indicating that quantum computing may be used to improve anomaly detection in cybersecurity applications.
Intrusion Detection Systems (IDS) play a fundamental role in safeguarding digital infrastructures against cyber threats. Quantum Machine Learning (QML) presents a promising frontier in this domain, offering potential computational speedups and improved management of complex datasets. However, its practical deployment is currently limited by the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. This work explores the application of various QML approaches, specifically Quantum Support Vector Classifiers (QSVC), Variational Quantum Classifiers (VQC), and hybrid quantum-classical neural networks for anomaly detection tasks. The models were fine-tuned and benchmarked using the ToN_IoT dataset, with performance assessed on IBM's noisy quantum simulators to evaluate their behavior under realistic noise conditions. Results indicate that the Pegasos-QSVC model outperformed others, reaching an accuracy of 93.05% and an F1 score of 93.73%. Through a comparative analysis of different circuit architectures, this study identifies strategies to improve the robustness of QML systems, emphasizing their emerging potential in cybersecurity scenarios.