Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.
Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.
Quantum machine learning deals with leveraging quantum theory with classic machine learning algorithms. Current research efforts study the advantages of using quantum mechanics or quantum information theory to accelerate learning time or convergence. Other efforts study data transformations in the quantum information space to evaluate robustness and performance boosts. This paper focuses on processing input data using randomized quantum circuits that act as quantum convolutions producing new representations that can be used in a convolutional network. Experimental results suggest that the performance is comparable to classic convolutional neural networks, and in some instances, using quantum convolutions can accelerate convergence.
Abstract Distributed Denial-of-Service (DDoS) attacks remain one of the most critical threats to modern cybersecurity. While machine learning techniques have proven effective for detection, classical approaches struggle with the growing complexity and scale of these attacks. Quantum computing, particularly quantum kernel methods, offers a promising alternative; however, the current state of the art faces a major challenge: vanishing similarity, which severely limits model expressiveness in high-dimensional spaces. This work introduces a novel quantum kernel inspired by multiple kernel learning, designed to mitigate vanishing similarity by constructing kernels in reduced-dimensional subspaces and combining them through averaging. The methodology is validated on the Canadian Institute for Cybersecurity dataset (NTP-based DDoS attacks). The proposed kernel effectively preserves classification capability in high-dimensional feature spaces, paving the way for practical applications of quantum kernels.
The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy of QML models such as QSVM and QCNN for URL detection in comparison to classical models remains unexplored. This study evaluates ML models (SVMs and CNNs) and QML models (QSVMs and QCNNs) on a dataset, employing data preprocessing techniques such as outliers, feature scaling and feature selection with ANOVA and PCA. Quantum models utilized ZZFeatureMap and ZFeatureMap for data encoding, to transfer original data to qubits. The achieved results showed that CNNs outperformed QCNNs and QSVMs outperformed SVMs in the performance evaluation, demonstrating a competitive potential of quantum computing. QML shows promise for cybersecurity, particularly given the QSVM’s kernel advantages, but current hardware limits the QCNN’s practicality. The significance of this research is to contribute to the growing body of knowledge in cybersecurity by providing a comparative analysis of classical and quantum ML models for classifying malicious URLs.
Quantum computing is progressing rapidly, which brings both new opportunities and significant cybersecurity risks. This matter is particularly crucial since traditional cryptographic systems will be vulnerable to attacks in the quantum era. At the same time, quantum machine learning (QML) is becoming a promising way to improve threat detection, anomaly analysis, and secure communication using quantum-inspired and hybrid learning methods. This paper aims to review essential QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Reinforcement Learning (QRL), while evaluating their prospective contributions to enhancing cybersecurity frameworks. We summarize recent simulation-based and early-stage experimental work, highlight their reported performance on benchmark datasets, and analyze major limitations related to hardware noise, data encoding bottlenecks, and scalability. The review also discusses unresolved issues and potential avenues for future research that could integrate quantum-safe and quantum-enhanced methods. The results indicate that, although the technology is still in its exploratory stage, QML has significant potential to enhance the creation of intelligent, resilient, and quantum-safe cybersecurity ecosystems.
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.
ABSTRACT Context The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or adapt effectively to evolving threat landscapes. Objective As a result, Quantum Machine Learning (QML) has emerged as a promising alternative, leveraging computational principles from quantum mechanics. QML enables more expressive encoding and efficient processing of high‐dimensional data, offering potential advantages for complex cybersecurity tasks. Method This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. Results It also maps these methods across supervised, unsupervised, and generative learning paradigms, as well as to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted‐traffic analytics. It also discusses their application in cloud computing security, where QML can enhance secure, scalable operations. Conclusion Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.