Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.
Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.
Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.
Abstract The rapid expansion of the Internet of Things (IoT) has produced a highly linked digital environment together with a growing number of complex cyber threats. This work presents a Quantum-Enhanced Hybrid Anomaly Detection Framework combining two advanced Quantum Machine Learning (QML) models: Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) to address these challenges. By projecting data into high-dimensional quantum spaces, QSVM efficiently classifies non-linear and low-frequency threats such as User to Root (U2R) and Probe attacks using quantum kernel functions. Concurrently, QNN models utilize Variational Quantum Circuits (VQCs) to detect large-scale threats like Denial of Service (DoS) and Remote to Local (R2L) attacks by offering superior generalization. Both models produce a final, high-confidence prediction by combining their outputs using a soft voting ensemble approach. The proposed framework guarantees scalability and resilience while ensuring compatibility with present simulation systems. The experimental results obtained indicate that the hybrid QSVM + QNN model significantly outperforms standalone models and conventional methods, providing a future-ready security solution for next-generation IoT infrastructures. In addition, this model delivers a robust, adaptable, and quantum-resilient security solution, well-suited for safeguarding future IoT ecosystems.
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.
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.
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 proliferation of Internet-connected devices has intensified the risk of large-scale network intrusions, necessitating more advanced detection methodologies for the post quantum era. This work presents, to the best of our knowledge, a comprehensive investigation of quantum machine learning (QML) for anomaly detection on the BoT-IoT dataset, a widely adopted benchmark for IoT security. Leveraging feature-engineered representations, we implement and evaluate four representative quantum classifiers: Quantum Support Vector Classification (QSVC), Quantum Neural Networks (QNN), and Variational Quantum Classifiers (VQC).