Phishing is a kind of cybercrime where attackers trick unsuspecting network users into revealing sensitive information, resulting in identity theft and financial damage. As this threat continues to grow, artificial intelligence strategies have emerged as a promising solution for its detection in recent years. However, conventional machine learning approaches are starting to show limitations because training on large datasets in standard computing environments may either take too long for accurate results or produce poor accuracy with quick training. The recent advances in the study of the application of quantum computing to machine learning tasks have demonstrated that existing solutions can be strengthened by utilizing the synergy of the two fields, known as Quantum Machine Learning (QML). To explore the potential of this emerging technique, this study implements various QML models integrated with the latest version of Qiskit and presents a detailed execution process along with a comprehensive comparative analysis. The quantitative results obtained from the experiments demonstrate the significant potential of QML models in phishing detection. Furthermore, these state-of-the-art assessments also contribute to future research in improving the composition of QML models applied in the cyber defense field.
Phishing is a kind of cybercrime where attackers trick unsuspecting network users into revealing sensitive information, resulting in identity theft and financial damage. As this threat continues to grow, artificial intelligence strategies have emerged as a promising solution for its detection in recent years. However, conventional machine learning approaches are starting to show limitations because training on large datasets in standard computing environments may either take too long for accurate results or produce poor accuracy with quick training. The recent advances in the study of the application of quantum computing to machine learning tasks have demonstrated that existing solutions can be strengthened by utilizing the synergy of the two fields, known as Quantum Machine Learning (QML). To explore the potential of this emerging technique, this study implements various QML models integrated with the latest version of Qiskit and presents a detailed execution process along with a comprehensive comparative analysis. The quantitative results obtained from the experiments demonstrate the significant potential of QML models in phishing detection. Furthermore, these state-of-the-art assessments also contribute to future research in improving the composition of QML models applied in the cyber defense field.
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.
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.
Emerging cyber threats demand novel detection and analysis paradigms that classical machine learning approaches are increasingly unable to address at requisite scale and speed. Quantum Machine Learning (QML), leveraging quantum mechanical phenomena such as superposition and entanglement, has emerged as a promising framework for cyber threat intelligence (CTI). This scoping review systematically examines 24 published works from 2020 to 2025 following the PRISMA-ScR protocol. We classify QML frameworks like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Circuits (VQC), and Quantum Convolutional Neural Networks (QCNN) across four primary CTI domains: intrusion detection, malware classification, anomaly detection, and phishing detection. Results show that QML models achieved up to 98% accuracy in intrusion detection and 97% in IoT anomaly detection on curated benchmark datasets. Critical caveats apply: 83.3% of studies relied exclusively on quantum simulators rather than real hardware, most benchmarks used datasets predating 2017, and the classical pre-processing and post-processing overheads in hybrid architectures substantially qualify reported speedup claims. We assess hardware constraints in Noisy Intermediate-Scale Quantum (NISQ) devices, data encoding bottlenecks, and missing quality benchmarks. NIST post-quantum cryptography standards (FIPS 203-205, August 2024) and IBM quantum hardware milestones are discussed as contextual anchors. A structured research agenda concludes the review.
Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and vari ational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.
Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms’ ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.
This is a planned study that compares how much does data encoding techniques affect Quantum Machine Learning (QML) performance compared to Classical Machine Learning (CML)? Unlike classical models, QML cannot directly read a dataset; it must first convert features into quantum states using conversion methods like Classical machine learning reads normal numbers directly, but Quantum machine learning first turns those numbers into quantum form (qubits) that can be present in multiple states at once. This strengthens core AI skills for data preprocessing, PCA (Principal Component Analysis) features, model training, and evaluation. These are the basic building blocks of both approaches, then testing them on the same data. I will compare common classical baselines against QML approaches to run on a simulator and will report prediction quality (F1 score). Moreover, practical evaluations like testing multiple encoding techniques under the same experimental conditions and focusing on encoding driven behavior and publishable contribution as it clearly guides which encoding works best for which dataset conditions. The poster will define the main terms for both CML and QML, showing how they are related to explaining the major differences. The project produces clear plots, and guidelines on which encoding choices are most reliable for security style datasets. Concerning education, this can be used as a teaching resource for AI evaluation and emerging QML workflows. In conclusion, this comparative analysis for a small AI and Security task (phishing detection using a tabular dataset of URL/email features) demonstrates strong skills in machine learning, and emerging QML tools that are valuable for research and development.