The escalating complexity and sophistication of cyber threats targeting Industrial Control Systems (ICS) demand innovative solutions to ensure robust security. Quantum computing, with its unprecedented computational capabilities, emerges as a promising technology in this domain. In this paper, we present an advanced stacking ensemble framework that integrates three quantum classifiers: the Quantum Variational Classifier (QVC), the Quantum Support Vector Classifier (QSVC), and the Quantum Neural Network (QNN). Our ensemble approach optimizes qubit efficiency while delivering superior classification performance. We evaluated our approach on a real-time, imbalanced ICS power system dataset comprising 78, 345 samples representing natural and attack scenarios. The results demonstrate that our advanced ensemble framework outperforms baseline quantum classifiers and achieves these results with minimal qubit usage. This demonstrates our approach's practical viability in enhancing cybersecurity in ICS through quantum computing.
The escalating complexity and sophistication of cyber threats targeting Industrial Control Systems (ICS) demand innovative solutions to ensure robust security. Quantum computing, with its unprecedented computational capabilities, emerges as a promising technology in this domain. In this paper, we present an advanced stacking ensemble framework that integrates three quantum classifiers: the Quantum Variational Classifier (QVC), the Quantum Support Vector Classifier (QSVC), and the Quantum Neural Network (QNN). Our ensemble approach optimizes qubit efficiency while delivering superior classification performance. We evaluated our approach on a real-time, imbalanced ICS power system dataset comprising 78, 345 samples representing natural and attack scenarios. The results demonstrate that our advanced ensemble framework outperforms baseline quantum classifiers and achieves these results with minimal qubit usage. This demonstrates our approach's practical viability in enhancing cybersecurity in ICS through quantum computing.
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.
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.
Innovative methods for information security and fraud prevention are required in today's digital environment due to the expanding volume of data and the increasing complexity of cyber threats. The use of quantum computing techniques to improve fraud detection and classification systems is investigated in this study. The study's machine learning framework integrates three distinct quantum algorithms to improve classification techniques. The first technique uses a Pauli feature map and a Quantum Support Vector Classifier (QSVC) that leverages a quantum kernel to transform classical input into quantum states. The second technique use a ZZ feature map with "linear" entanglement and a support vector classifier model, utilizing quantum kernels to enhance quantum systems. The third method utilizes Variational Quantum Circuits (VQC) with actual amplitudes, which integrate quantum and conventional machine learning techniques to provide optimized classification. The best results were obtained by the QSVC using ZZ feature maps and linear entanglement, which had a precision of 1.0 and a notable decrease in false positives. In order to improve fraud detection systems' accuracy and dependability and offer strong solutions to financial institutions, this study shows how quantum computing has the potential to completely transform cybersecurity.
The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and heightened sensitivity to anomalous behavior.Quantum Machine Learning (QML), an emerging interdisciplinary field at the intersection of quantum computing and artificial intelligence, presents a promising avenue for augmenting threat detection mechanisms. Leveraging quantum phenomena such as superposition and entanglement, QML models offer potential advantages in processing speed, pattern recognition, and feature space transformation that can outperform their classical counterparts in high-dimensional data analysis. This paper explores the application of QML to threat detection in high-security networks, proposing a hybrid quantum-classical framework that integrates quantum-enhanced classifiers such as quantum support vector machines and variational quantum circuits into existing detection pipelines.The study outlines a technical overview of quantum computing principles relevant to cybersecurity, critically evaluates existing detection architectures, and presents simulation-based case studies to assess performance metrics, including detection accuracy and false positive rates. It further examines the limitations of current quantum hardware, algorithmic constraints, and emerging ethical and operational considerations. The findings suggest that while QML is still constrained by hardware maturity and integration complexity, it holds transformative potential for proactive, intelligent, and adaptive cyber defense systems in high-stakes environments. This research contributes to ongoing efforts to future-proof cybersecurity infrastructure against both classical and post-quantum threat landscapes.
The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and adversarial threat management. The framework also incorporates Quantum Reinforcement Learning (QRL) for autonomous incident response, a Quantum Authentication module for securing identity verification using biometric and behavioral data, and a Policy Compliance Interface powered by Quantum Compliance Analyzers for regulatory adherence. Experimental results demonstrated substantial improvements in cybersecurity metrics, including a 96% accuracy in threat detection, a 28% reduction in incident response time, and a 96% success rate in compliance simulations. These findings underscore the framework's capacity to offer adaptive, scalable, and efficient cybersecurity solutions tailored to modern challenges. This study provides a significant step toward integrating quantum technologies into practical cybersecurity applications, paving the way for future innovations in intelligent, secure, and adaptable defense systems.
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies