The rapid growth of Internet of Things (IoT) deployments has increased the need for effective intrusiondetection methods; however, most Quantum Machine Learning (QML) intrusion-detection studies still rely on homogeneous feature encodings, limiting their performance on unseen attack scenarios. In this paper, we introduce two heterogeneous quantum fusion methods, Quantum Voting Ensemble (QVE) and Quantum Weighted Ensemble (QWE), that combine QSVC classifiers built from distinct feature maps to exploit encoding-level diversity. On the 5-class IoTID20 dataset, across 10 to 16 qubits and 25 model settings, QVE achieves 99.5% accuracy and 0.971 Matthews Correlation Coefficient (MCC), the best evaluated quantum result, within 0.2 percentage points of classical Random Forest, and 4.2 to 7.8 percentage points above Quantum Random Forest (QRF), indicating that heterogeneous encodings are more effective for this task than homogeneous quantum bagging.
The rapid growth of Internet of Things (IoT) deployments has increased the need for effective intrusiondetection methods; however, most Quantum Machine Learning (QML) intrusion-detection studies still rely on homogeneous feature encodings, limiting their performance on unseen attack scenarios. In this paper, we introduce two heterogeneous quantum fusion methods, Quantum Voting Ensemble (QVE) and Quantum Weighted Ensemble (QWE), that combine QSVC classifiers built from distinct feature maps to exploit encoding-level diversity. On the 5-class IoTID20 dataset, across 10 to 16 qubits and 25 model settings, QVE achieves 99.5% accuracy and 0.971 Matthews Correlation Coefficient (MCC), the best evaluated quantum result, within 0.2 percentage points of classical Random Forest, and 4.2 to 7.8 percentage points above Quantum Random Forest (QRF), indicating that heterogeneous encodings are more effective for this task than homogeneous quantum bagging.
Intrusion Detection Systems (IDSs) must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether heterogeneous quantum learners can provide useful and non-redundant decision information for IDS tasks. We study Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs), which rely on different learning mechanisms and exhibit distinct prediction behaviors. To combine these models, we propose the System-Level Meta-Quantum Ensemble (MQE), a hybrid quantum-classical framework that fuses QSVM and QNN outputs using a Random Forest meta-learner. The meta-learner captures agreement and disagreement patterns between the quantum branches to improve prediction stability and detection performance. Experiments on TON IoT and CICIDS2017 show that MQE improves selected performance, low-FPR, and reliability metrics over several standalone quantum learners, with gains depending on the dataset, metric, and fusion representation. The results highlight meta-level fusion as a practical strategy for building more reliable QML-based IDS pipelines.
Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single quantum embedding strategy, limiting representation diversity. This paper proposes a Multi-Embedding Quantum Ensemble (ME-QE) framework that combines Angle Embedding and IQP Embedding using concatenation and weighted fusion strategies. The proposed approach is evaluated on the NSL-KDD dataset for binary intrusion detection using stratified 5-fold cross-validation. Experimental results show that the best fusion configuration achieves 94.75% accuracy, while the classical Random Forest baseline reaches 97.50%. The analysis further demonstrates that Angle Embedding contributes more effectively to classification performance than IQP Embedding in this context. In addition, the study highlights important scalability limitations of quantum kernel methods due to the computational cost of pairwise kernel evaluation. The proposed framework provides insights into hybrid quantum–classical intrusion detection and establishes a foundation for future scalable quantum cybersecurity architectures.
This survey examines quantum-enhanced machine learning techniques for intrusion detection in Internet of Things (IoT) and fog environments, synthesizing recent work on hybrid quantum-classical models, quantum convolutional neural networks (QCNNs), quantum support vector machines (QSVMs), and quantum-assisted clustering.We review five recent primary studies that apply quantum methods to IoT security, summarize their methodological choices (encoding, circuit ansatz, classical-quantum interfaces), and evaluate reported benefits and limitations with respect to accuracy, computational cost, and deployability in resource-constrained settings.The literature indicates potential gains in feature expressivity and false-negative reduction from quantum encodings and variational circuits, but practical barriers remain: limited qubit counts, noisy hardware, encoding overhead, and scarce real-world deployment studies.We identify concrete research gaps and propose directions for lightweight encodings, noise-aware hybrid architectures, federated quantum-assisted Intrusion Detection System (IDS), and standardized benchmarks for reproducible evaluation.The survey aims to guide researchers and practitioners toward scalable, explainable, and deployable quantum-enhanced intrusion detection for IoT.
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) 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 (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.