This chapter provides a comprehensive overview of the recent developments in quantum machine learning for intrusion detection systems. The authors review the state of the art based on the published work “Quantum Machine Learning for Intrusion Detection of Distributed Denial of Service Attacks: A Comparative View” and its relevant citations. The chapter discusses three quantum models, including quantum support vector machines, hybrid quantum-classical neural networks, and a two-circuit ensemble model, which run parallel on two quantum processing units. The authors compare the performance of these models in terms of accuracy and computational resource consumption. Their work demonstrates the effectiveness of quantum models in supporting current and future cybersecurity systems, achieving close to 100% accuracy, with 96% being the worst-case scenario. The chapter concludes with future research directions for this promising field.
This chapter provides a comprehensive overview of the recent developments in quantum machine learning for intrusion detection systems. The authors review the state of the art based on the published work “Quantum Machine Learning for Intrusion Detection of Distributed Denial of Service Attacks: A Comparative View” and its relevant citations. The chapter discusses three quantum models, including quantum support vector machines, hybrid quantum-classical neural networks, and a two-circuit ensemble model, which run parallel on two quantum processing units. The authors compare the performance of these models in terms of accuracy and computational resource consumption. Their work demonstrates the effectiveness of quantum models in supporting current and future cybersecurity systems, achieving close to 100% accuracy, with 96% being the worst-case scenario. The chapter concludes with future research directions for this promising field.
In recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems’ vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid QuantumClassical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models’ effectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models’ performance in terms of accuracy and consumption of computational resources.
Quantum computing presents potential advantages over classical computing in terms of computational complexity. Therefore, it is expected for quantum machine learning applications to have improvements in capacity and learning efficiency over classical machine learning methods. This paper aims to present a Systematic Literature Review of articles published between 2017 and 2022, identifying, analyzing, and comparing different proposals of quantum machine learning applications for network intrusion detection systems (IDS). This study focused on identifying papers that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms found were variational hybrid quantum-classical, with models based on quantum support vector machines and quantum neural networks. Benefits compared to classical models were observed and described, such as reduced training time and improved classification accuracy for attacking traffic.
With the potential for quantum computing to completely transform cybersecurity, quantum machine learning is becoming a ground-breaking technology. Cyber-attacks have been successfully countered by traditional network defense systems, which mostly use conventional machine learning (ML) techniques. However, the growing complexity of assaults and the exponential expansion of network data reveal the shortcomings of traditional methods, especially with regard to speed and scalability. By utilizing quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Variational Quantum Circuits (VQC), Quantum Machine Learning (QML) presents a viable substitute. These methods are especially well-suited for real-time network defense scenarios since they show the capacity to handle high-dimensional data, identify complex patterns, and increase computational efficiency. In this research, the effectiveness of QML techniques in network defense is thoroughly examined and contrasted with that of traditional machine learning techniques. The study emphasizes how QML may surpass conventional models in terms of accuracy, scalability, and resilience against sophisticated threats by concentrating on use cases like intrusion detection, malware analysis, and anomaly detection. The necessity for hybrid quantum-classical models and the present constraints of quantum hardware are among the difficulties that are discussed in the study. This study highlights the revolutionary potential of QML in strengthening network defense mechanisms and identifies crucial areas for further research, paving the road for a secure digital future by combining theoretical ideas with experimental discoveries.
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
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.
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.