Aug 2026· Women in Optics and Photonics in India· Vol 14308, pp. 143080X - 143080X-7· 0 citations· 9 references
Engineering
TL;DR
This work proposes a simulation-based Quantum Intrusion Detection framework using the principles of quantum computing, with the main focus on multi-qubit entanglement and superposition to improve the detection of network anomalies.
Abstract
Conventional intrusion detection systems lack the ability to detect advanced cyberattacks because they rely on traditional computational models and linear correlations of features. This work proposes a simulation-based Quantum Intrusion Detection framework using the principles of quantum computing, with the main focus on multi-qubit entanglement and superposition to improve the detection of network anomalies. The multi-layered quantum circuit has three unique quantum state configurations: extended Bell states for 4-qubit pairwise correlations, Greenberger-Horne-Zeilinger (GHZ) states for 6-qubit multipartite entanglement and combined 8-qubit circuits with Quantum Fourier Transform for temporal analysis. The pipeline first converts classical network traffic features to quantum-compatible representations using statistical aggregation and correlation matrix construction. Cybersecurity datasets are handled by translating network features and grouping them into protocol type, packet size, interarrival time, source IP entropy, destination port, TCP flag, payload entropy, and flow duration using statistical aggregation. Quantum entanglement breaking is the main indicator of network anomaly, where malicious traffic patterns disturb the learned quantum correlations in baseline training done using normal traffic. The anomaly detection utilizes multi-level quantum analysis that integrates probability divergence measurement by Hellinger distance, entanglement deviation monitoring via concurrence computation and correlation breaking detection between quantum state pairs. The system uses weighted decision fusion with increased weight on 8-qubit unified circuit results. The system is evaluated through simulation using the NSL-KDD dataset, achieving an overall detection accuracy of 85%. This work serves as a baseline for investigating how quantum computing can be used in the field of cybersecurity in future quantum-enabled environments.
Quantum machine learning (QML) is increasingly investigated for network intrusion detection, but the practical value of current quantum models remains uncertain because many studies rely on small simulated data sets, idealized execution, weak classical baselines, or isolated accuracy comparisons. This study develops and evaluates a resource-aware hybrid quantum-classical intrusion detection framework that combines fixed fidelity quantum kernels, repaired Quantum Kernel Alignment (QKA), a Variational Quantum Classifier (VQC), LightGBM, prototype-based kernel reduction, and probabilistic stacking. Experiments use the UNSW-NB15 benchmark under a leakage-aware preprocessing protocol and repeated evaluation across five random seeds. The experimental program covers 4- and 6-qubit representations, multiple training sizes, prototype budgets, and finite-shot configurations, yielding 260 experimental configurations/runs. The strongest quantum-assisted model, a 6-qubit QKA-VQC-LightGBM stack at 100 samples per class, obtains mean F1=0.8480, MCC=0.7092, ROC-AUC=0.9398, and PR-AUC=0.9302. Repaired QKA improves F1 from 0.8043 for the corresponding fixed kernel to 0.8089, while 50-prototype QKA attains F1=0.8257 with a more favorable kernel-workload trade-off than larger prototype budgets. Finite-shot experiments between 1,024 and 4,096 shots remain stable in simulation. However, full-data LightGBM remains stronger overall (F1=0.8957, MCC=0.8068, ROC-AUC=0.9755, PR-AUC=0.9704), and corrected statistical tests do not establish general quantum superiority. These findings motivate a Selective Quantum Intrusion Detection System (S-QIDS), in which classical inference handles high-confidence traffic and quantum processing is reserved for uncertain, novel, or high-risk observations. The study therefore reframes near-term quantum intrusion detection from unconditional quantum advantage toward measurable quantum utility, quantum-classical complementarity, and resource-aware quantum intervention.
Rahul R. Bhoge, R. Keole, Pravin P. Karde· International journal of com...· 0 citations
The number of digital communications and cloud infrastructure is rising rapidly, cyber attacks have become much more advanced and frequent. Conventional approaches for intrusion detection cannot detect the evolving threats due to highly dimensional traffic patterns and complex attacker behavior. To tackle these issues, we introduce a hybrid quantum-classical intrusion detection approach by integrating classical and quantum machine learning algorithms to build an efficient intrusion detection system. Our model takes advantage of UNSW-NB15 Cybersecurity benchmark dataset that contains modern network attack vectors including exploit, denial of service attack, reconnaissance, shellcode, and backdoor attacks. For data preparation, we implement various data processing methods including categorical encoding, feature scaling, and dimensionality reduction. For comparison purposes, Random Forest algorithm is used as a classical detection method and variational quantum classifier based on Qiskit software library is utilized as our quantum learning model. Our experimental results show that our hybrid model is highly accurate in detecting the attacks. This work provides a comparative analysis between classical and quantum learning models demonstrating the promising future of quantum computers in cyber attacks detection tasks.
Pravin P, Kuldeep Chowdary Raavi, Meher Deepika Uppaluri et al.· 2026 7th International Confe...· 0 citations
The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware, and offers viable post-quantum hardening for security-critical applications.
Soha Rawas, Mohammed Al Saleh, A. D. Samala et al.· Applied Computing and Inform...· 0 citations
The proposed framework can improve threat detection and system resilience in critical infrastructure contexts and support the use of kernel-based quantum-inspired representations as a tunable early-warning layer for PSAP traffic monitoring, while also showing that threshold calibration and operational context remain necessary before deployment.
Carlos B. Rosa-Remedios, P. Caballero-Gil, J. Molina-Gil· Computers, Materials & C...· 0 citations
: The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.
Alanoud Al Mazroa, A. Alamoudi, N. Karabayev et al.· Computers, Materials & C...· 0 citations
Modern botnets use encrypted Command-and-Control (C2) communications, which poses a major challenge for network security given the fact that payloads are encrypted, which makes the use of traditional signature-based inspection methods ineffective. In this work, we introduce QSVM Shield, a hybrid quantum-classical solution for encrypted botnet C2 traffic detection purely based on network flow metadata following a Quantum Support Vector Machine (QSVM) algorithm. The proposed technique does not need to decrypt the packet payloads to detect threats, but instead uses statistical characteristics of encrypted packet flows to achieve this goal while maintaining the privacy of the communications. First, the scale variations in the essential flow attributes are reduced and feature consistency is enhanced by applying logarithmic feature transformation and MinMax normalization. Principal Component Analysis (PCA) is then used to find a low dimensional representation that is compact enough for quantum encoding. The resulting reduced feature vectors are then loaded into a 4-qubit quantum feature map that is implemented using PennyLane, with parameterized quantum rotations and entanglement operations building up a nonlinear quantum kernel used to model complex relationships between the various encrypted traffic samples. The generated kernel matrix is used with a classical Support Vector Classifier that has a precomputed kernel to classify benign traffic from encrypted C2 communication of botnets. For a practical security operation, all the framework is deployed as a Flask based web application, where it enables single instance prediction, batch CSV analysis, performance visualization and scan history management. Experimental results with network flow data encrypted under the proposed hybrid quantum-classical pipeline are presented and show that the proposed hybrid quantum-classical pipeline can achieve effective detection and discrimination of malicious C2 traffic while keeping the inference process lightweight, using only flow-level information. The combination of quantum kernel learning and privacy-preserving encrypted traffic analysis creates a technically secure and practically viable solution to next generation intelligent network intrusion detection.
J. D. S. Kumar, N. Vijay, Farooq Sunar Mahammad et al.· 2026 International Conferenc...· 0 citations
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