Cyberattacks are increasing in sophistication. As a result, traditional cybersecurity systems based on classical computing and conventional deep-learning techniques are increasingly exhibiting limitations and vulnerabilities. Although deep-learning models offer substantial advantages over traditional methods for automated intrusion detection through large-scale security data analysis and pattern recognition, they suffer from notable constraints related to high-dimensional feature spaces, scalability, and stringent real-time inference requirements. This work proposes a quantum-enhanced deep learning (QEDL) framework for detecting and mitigating real-time cyberattack patterns. Quantum computing technologies—specifically quantum feature selection and quantum data encoding—are integrated into a classical–hybrid quantum deep-learning architecture. The quantum approximate optimization algorithm (QAOA) is employed for quantum feature selection to enable efficient identification of discriminative features from high-dimensional cybersecurity datasets. In addition, quantum data encoding exploits superposition and entanglement to process multiple complex inputs simultaneously. The resulting quantum feature representations are processed using parameterized quantum circuits (PQCs), followed by classical deep-learning layers for accurate threat classification. The proposed QEDL framework is evaluated using the CIC–IDS2017 dataset, and its performance is compared against classical deep-learning models and pure quantum neural networks. Experimental results demonstrate that, within the proposed hybrid quantum–classical framework, the QAOA-based feature selection strategy achieved higher classification performance than the evaluated classical feature selection methods under the reported experimental settings. In addition, the proposed framework exhibited improved generalization to previously unseen attacks. These findings suggest that the proposed hybrid quantum–classical framework is a promising approach for cyberattack detection under the experimental conditions considered in this study. The experiments in this study were simulated using IBM Aer backend, without physical quantum hardware. The results obtained in this study serve as a simulation-based proof of concept.
Cyberattacks are increasing in sophistication. As a result, traditional cybersecurity systems based on classical computing and conventional deep-learning techniques are increasingly exhibiting limitations and vulnerabilities. Although deep-learning models offer substantial advantages over traditional methods for automated intrusion detection through large-scale security data analysis and pattern recognition, they suffer from notable constraints related to high-dimensional feature spaces, scalability, and stringent real-time inference requirements. This work proposes a quantum-enhanced deep learning (QEDL) framework for detecting and mitigating real-time cyberattack patterns. Quantum computing technologies—specifically quantum feature selection and quantum data encoding—are integrated into a classical–hybrid quantum deep-learning architecture. The quantum approximate optimization algorithm (QAOA) is employed for quantum feature selection to enable efficient identification of discriminative features from high-dimensional cybersecurity datasets. In addition, quantum data encoding exploits superposition and entanglement to process multiple complex inputs simultaneously. The resulting quantum feature representations are processed using parameterized quantum circuits (PQCs), followed by classical deep-learning layers for accurate threat classification. The proposed QEDL framework is evaluated using the CIC–IDS2017 dataset, and its performance is compared against classical deep-learning models and pure quantum neural networks. Experimental results demonstrate that, within the proposed hybrid quantum–classical framework, the QAOA-based feature selection strategy achieved higher classification performance than the evaluated classical feature selection methods under the reported experimental settings. In addition, the proposed framework exhibited improved generalization to previously unseen attacks. These findings suggest that the proposed hybrid quantum–classical framework is a promising approach for cyberattack detection under the experimental conditions considered in this study. The experiments in this study were simulated using IBM Aer backend, without physical quantum hardware. The results obtained in this study serve as a simulation-based proof of concept.
Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection and explore possible quantum advantages in cybersecurity analytics. Using the UNSW-NB15 dataset, network traffic is transformed into structured feature vectors through classical preprocessing and normalization. Classical models, including Logistic Regression and Support Vector Machines with linear and RBF kernels, are evaluated on the full dataset to establish baseline performance under large-sample conditions. Simultaneously, a quantum-enhanced pipeline maps classical features into variational quantum circuits via angle encoding and entangling layers, executed on a CPU-based quantum simulator, with resulting quantum embeddings classified using a classical SVM. Experiments show that while classical models achieve higher overall accuracy with large datasets, quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce, suggesting that quantum feature spaces capture complex correlations inaccessible to shallow classical models. These results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks and provide a reproducible framework for evaluating quantum advantages as quantum hardware and simulators continue to advance.
The usual cybersecurity techniques are finding it increasingly difficult to keep up with the increasing complexity and repetitiveness of cyberattacks. Despite their cost, traditional machine learning models constantly have trouble identifying hard and complex dangers like advanced persistent threats (APTs) and zero-day exploits because of their limitations in pattern recognition, feature generalization, and computing scalability. Real and promising answers to these limitations and constraints can be found in quantum computing, which has a radically new computational intelligence paradigm founded on the ideas and concepts of superposition, entanglement, and quantum parallelism. Specifically, by fast and speeding up learning procedures and identifying different patterns in high-dimensional data that traditional and the usual approaches frequently overlook, the use of Quantum Machine Learning (QML) offers the possibility of improved and greater threat identification features This research explores how QML can be integrated into cybersecurity frameworks to identify and detect and mitigate advanced cyber threats. Through theoretical analysis and proper simulation-based validation, the study investigates the viability, benefits, and limitations or constraints of QML-based cybersecurity systems in both current and near-future computational contexts.
The emergence of quantum computing introduces significant challenges to traditional cybersecurity mechanisms, requiring innovative approaches for post-quantum environments. The main purpose of the study is to design a Quantum-Enhanced Adversarial Defense (QEAD) framework that integrates Quantum Machine Learning (QML) with Adversarial Deep Learning (ADL) to detect both classical and quantum-level cyberattacks. The research utilizes a simulated dataset containing post-quantum attack vectors and evaluates three models: Classical Deep Learning (DL), Quantum ML, and the proposed Hybrid QEAD. The experimental results show that the Hybrid QEAD model achieves the highest detection accuracy (91.44%), outperforming Classical DL and Quantum ML in energy efficiency, with statistically significant improvements (ANOVA, p < 0.05). The quantum properties of superposition and entanglement enhance feature representation and parallel computation, resulting in faster and more accurate threat detection. To address current hardware limitations such as circuit depth and quantum noise, the framework explores practical mitigation strategies including zero-noise extrapolation (ZNE), probabilistic error cancellation (PEC), and noise-aware variational circuit training. Despite these constraints, QEAD demonstrates viable deployment potential in prequantum transition infrastructures. The findings establish QEAD as a scalable solution for post-quantum cybersecurity, with future applications in Quantum Blockchain, Federated Quantum Learning, and Quantum IoT Security.
The emergence of quantum computing introduces significant challenges to traditional cybersecurity mechanisms, requiring innovative approaches for post-quantum environments. The main purpose of the study is to design a Quantum-Enhanced Adversarial Defense (QEAD) framework that integrates Quantum Machine Learning (QML) with Adversarial Deep Learning (ADL) to detect both classical and quantum-level cyberattacks. The research utilizes a simulated dataset containing post-quantum attack vectors and evaluates three models: Classical Deep Learning (DL), Quantum ML, and the proposed Hybrid QEAD. The experimental results show that the Hybrid QEAD model achieves the highest detection accuracy (91.44%), outperforming Classical DL and Quantum ML in energy efficiency, with statistically significant improvements (ANOVA, p < 0.05). The quantum properties of superposition and entanglement enhance feature representation and parallel computation, resulting in faster and more accurate threat detection. To address current hardware limitations such as circuit depth and quantum noise, the framework explores practical mitigation strategies including zero-noise extrapolation (ZNE), probabilistic error cancellation (PEC), and noise-aware variational circuit training. Despite these constraints, QEAD demonstrates viable deployment potential in prequantum transition infrastructures. The findings establish QEAD as a scalable solution for post-quantum cybersecurity, with future applications in Quantum Blockchain, Federated Quantum Learning, and Quantum IoT Security.
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.
There is a pressing need for anomaly detection algorithms that can handle large amounts of diverse traffic data due to the quick growth of networked systems and the sophistication of contemporary cyberattacks. Despite the widespread use of conventional machine learning techniques for network intrusion detection, feature representation constraints, dataset imbalance, and the computational expense of scaling to vast problem spaces sometimes restrict their efficacy. In the meantime, new developments in quantum-assisted computation have opened up exciting possibilities for using quantum state changes to improve the feature learning process. Using the NF-UNSWNB15-v2 benchmark, this research presents HybridQCNN, a unique quantum-classical computing framework for binary classification of network traffic.The proposed architecture combines standard preprocessing techniques with an 8-qubit Parameterized Quantum Circuit (PQC) featuring multiple variational layers, where IsingXX and IsingYY entangling operations are applied to encode interfeature correlations, and a subsequent fully connected classical layer handles the final binary decision. A rigorous leakage-free data partitioning protocol was enforced, ensuring that all preprocessing transformations—including feature encoding, normalization, and threshold computation—were derived exclusively from the training subset, thereby preventing any information leakage from held-out partitions during evaluation. Experimental validation on a class-balanced subset of 60,000 traffic records yielded a classification accuracy of 97.33%, an F1-score of 0.9730, and a malicious traffic detection precision of 98.38%. Furthermore, the model achieved an AUC-ROC of 0.9861, reflecting strong class-level discriminability. Despite this performance, the entire model contains only 489 trainable parameters, underscoring the parameter efficiency of hybrid architectures relative to conventional deep learning baselines. These results suggest that integrating variational quantum circuits with classical inference modules provides an effective and scalable solution for network anomaly detection.