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.
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.
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.
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.
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.
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.
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.
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.