Under tight feature and compute budgets, classical threat detection pipelines often degrade on near-decision-boundary events. Small quantum processors are now available, but existing work inadequately shows whether quantum components improve end-to-end threat detection under the above resource constrained conditions. This paper tries to address this gap with a hybrid architecture that uses a compact multilayer perceptron layer to compress the information in data and then routes the processed features to a few qubit quantum heads implemented in quantum support vector machine (QSVM) and variational quantum circuit (VQC) models. On a simulation platform, we benchmark these hybrid models against classical models with comparable parameter budgets on two representative cybersecurity tasks, network intrusion detection on NSL-KDD dataset and spam filtering on Ling-Spam dataset. To validate their precision on real quantum hardware, we deploy the best 4-qubit QSVM model on an IBM Quantum device with noise-aware execution, evaluated on a smaller sub-dataset. In the results, shallow quantum heads consistently match, and on difficult near-boundary cases modestly reduce missed attacks and false alarms compared to classical models using the same features. Hardware validation results track the simulation behavior closely enough that the remaining gap is dominated by device noise rather than model design. Furthermore, we conduct adversarial attacks to test the robustness of one QSVM model. Taken together, the study shows that even on small, noisy devices, carefully engineered quantum components may function as competitive, budget-aware components in practical cyber threat detection applications.
Under tight feature and compute budgets, classical threat detection pipelines often degrade on near-decision-boundary events. Small quantum processors are now available, but existing work inadequately shows whether quantum components improve end-to-end threat detection under the above resource constrained conditions. This paper tries to address this gap with a hybrid architecture that uses a compact multilayer perceptron layer to compress the information in data and then routes the processed features to a few qubit quantum heads implemented in quantum support vector machine (QSVM) and variational quantum circuit (VQC) models. On a simulation platform, we benchmark these hybrid models against classical models with comparable parameter budgets on two representative cybersecurity tasks, network intrusion detection on NSL-KDD dataset and spam filtering on Ling-Spam dataset. To validate their precision on real quantum hardware, we deploy the best 4-qubit QSVM model on an IBM Quantum device with noise-aware execution, evaluated on a smaller sub-dataset. In the results, shallow quantum heads consistently match, and on difficult near-boundary cases modestly reduce missed attacks and false alarms compared to classical models using the same features. Hardware validation results track the simulation behavior closely enough that the remaining gap is dominated by device noise rather than model design. Furthermore, we conduct adversarial attacks to test the robustness of one QSVM model. Taken together, the study shows that even on small, noisy devices, carefully engineered quantum components may function as competitive, budget-aware components in practical cyber threat detection applications.
Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware, yet even strong classical systems still miss rare or borderline attacks when the data distribution drifts. Small, near-term quantum processors are now available, but existing work rarely shows whether quantum components can improve end-to-end detection under these unstable, resource constrained conditions rather than just adding complexity. We address this gap with a hybrid architecture that uses a compact multilayer perceptron to compress security data and then routes a few features to 2-4 qubit quantum heads implemented as quantum support vector machines and variational circuits. Under matched preprocessing and training budgets, we benchmark these hybrids against tuned classical baselines on two security tasks, network intrusion detection on NSL-KDD and spam filtering on Ling-Spam datasets, and then deploy the best 4-qubit quantum SVM to an IBM Quantum device with noise-aware execution (readout mitigation and dynamical decoupling). Across both datasets, shallow quantum heads consistently match, and on difficult near-boundary cases modestly reduce, missed attacks and false alarms relative to classical models using the same features. Hardware results track simulator behavior closely enough that the remaining gap is dominated by device noise rather than model design. Taken together, the study shows that even on small, noisy chips, carefully engineered quantum components can already function as competitive, budget-aware elements in practical threat detection pipelines.
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.
Intrusion detection systems (IDS) are essential for identifying cyber threats in complex digital environments. Machine learning (ML) is widely used to improve IDS by detecting anomalies, but classical ML methods often struggle with high-dimensional data and evolving threats. Quantum machine learning (QML) has been proposed as a potential paradigm to overcome some of these limitations, but is constrained by noisy intermediate-scale quantum (NISQ) challenges, affecting quality. This study systematically evaluates three QML models, Pegasos Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and a Hybrid Quantum–Classical Neural Network (HQNN), for network anomaly detection. The models were optimized and tested on the ToN_IoT and NSL-KDD datasets using IBM quantum simulators under both ideal and noisy conditions. Performance was analyzed through the F1-score distribution as a function of circuit complexity, revealing how entanglement and noise affect robustness across different backends. Comparisons with classical models contextualize the current maturity of QML for cybersecurity, while computational time was used as an indicator of model complexity to explore accuracy–efficiency trade-offs. Among all configurations, Pegasos-QSVC achieved the best results, with 94.60% accuracy and an F1-score of 94.13%. The findings provide practical guidelines for designing noise-resilient QML models and highlight their potential for reliable intrusion detection under realistic quantum conditions.
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.
In cybersecurity, protecting sensitive data from malicious attacks is of paramount importance. Classical machine learning algorithms have shown limitations in this domain due to their inability to handle complex, highly dynamic, and adversarial attack scenarios. Quantum machine learning (QML) algorithms, with their enhanced processing capacity and robustness against such attacks, offer a promising alternative to address these shortcomings. Consequently, QML can significantly improve the detection and mitigation of sophisticated and evolving threats. This study explores the application of QML algorithms, specifically the quantum support vector machine (QSVM) and a hybrid quantum neural network (QNN) and QSVM architecture (QSVM-QNN), on two real-world cybersecurity datasets: APA-DDoS and CIC-IoT2022. Initially, different qubit counts and encoding techniques are investigated to assess the effectiveness of the swap test and UU† methods for inner product calculation, which is later employed in the QSVM algorithm. Subsequently, the performance of the proposed QSVM and QSVM-QNN models is evaluated and compared against classical counterparts across varying numbers of qubits and encoding schemes. The results show that the proposed QSVM model achieved accuracies of 1.000 and 0.985, while the QSVM-QNN model reached accuracies of 0.970 and 1.000, respectively, on the two datasets, demonstrating strong performance and surpassing several classical models. Furthermore, to assess robustness, the proposed algorithms were tested under various noise models, highlighting their capability to provide effective solutions for cybersecurity challenges in realistic quantum noisy environments.
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.