Jul 2026· Anais do I Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ 2026)· pp. 48-58· 0 citations· 31 references
TL;DR
This paper investigates whether quantum principal component analysis can provide useful features for IDS without relying on claims of end-to-end quantum superiority, and finds that QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.
Abstract
Intrusion detection systems must balance predictive quality, robustness, and computational cost, yet the role of quantum representations under NISQ constraints remains unclear. This paper investigates whether quantum principal component analysis (QPCA) can provide useful features for IDS without relying on claims of end-to-end quantum superiority. We evaluate PCA- and QPCA-based pipelines combined with Logistic Regression, SVM, and Random Forest, and include a QPCA→VQC branch as a comparative quantum arm. Experiments on CICIDS2017 and NSL-KDD under nisq preset and scaled preset use simulator-based quantum execution, multi-seed evaluation, Wilcoxon–Holm tests, bootstrap confidence intervals, and cost analysis. Results show no universal advantage of QPCA, but selective ranking gains: ROC-AUC improves from 0.5397 to 0.8772 (CICIDS2017) and from 0.7453 to 0.8063 (NSL-KDD), both with corrected significance under matched data budgets. Overall, QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.
This paper presents a comparative benchmarking study of classical and quantum machine learning models for intrusion detection using three benchmark datasets: NSL-KDD, UNSW-NB15, and MQTTEEB-D2025. The study evaluates how preprocessing choices, feature selection strategies, and quantum encoding methods influence model performance across datasets with different levels of noise and complexity. A unified pipeline is adopted, incorporating normalization, imbalance handling, dimensionality reduction, and two feature selection approaches: Random Forest importance and a quantum-aware method based on Quantum Kernel Alignment with Mutual Information. Four models are assessed: Support Vector Machine, Random Forest, Quantum Support Vector Machine, and Pegasos Quantum SVM. Results show that classical models remain stable across datasets, while quantum models are more sensitive to feature representation and kernel alignment. Quantum performance improves significantly with quantum-aware feature selection, particularly on cleaner datasets, whereas heterogeneous datasets remain challenging. Pegasos Quantum SVM offers a favorable balance between accuracy and computational efficiency, highlighting the importance of preprocessing alignment for practical quantum intrusion detection.
Taha M. Mahmoud, N. Kaabouch· 2026 6th 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, Agariadne Dwinggo Samala et al.· Applied Computing and Inform...· 0 citations
A quantum-attribution audit is introduced that quantifies how much of any gain is genuinely attributable to the quantum component of quantum models, and attributes this to classical preprocessing and regularisation rather than quantum effects.
Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah et al.· 0 citations
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.
Harshini K, I. Jahan M A, Gaurav Kumar Bharti· Women in Optics and Photonic...· 0 citations
This work investigates variational quantum autoencoders (QAE) for one-class anomaly detection under strict parametric constraints, shifting the evaluation focus from absolute performance to performance–capacity tradeoffs. We implement a pure QAE baseline and a Hybrid QAE with classical compression/decoding around a quantum latent block, comparing both against classical baselines - AE, VAE, Isolation Forest, and One-Class SVM - on the NSL-KDD and ECG5000 benchmarks. The central contribution is a fairnessoriented evaluation protocol combining standard detection metrics (AUC-ROC, AUC-PR, and F1) with a performance-density measure (AUC-ROC per 1,000 trainable parameters) and a fair-budget comparison against a compact classical AE. On NSL-KDD, the QAE baseline achieves 0.9612 AUC-ROC with only 476 parameters versus 0.9679 for a classical AE with 5,580 parameters, while attaining 2.0193 AUC-ROC/1k parameters against 0.1735 for the classical AE - an order-of-magnitude improvement in parametric efficiency. Under a fairbudget setting, the QAE baseline also surpasses the compact classical AE on NSL-KDD (0.9612 vs. 0.9405). In contrast, ECG5000 favors classical methods, indicating domain dependence rather than universal quantum advantage. Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.
Murilo Salem, D. Pontes, João Carrett et al.· Anais do I Simpósio Brasilei...· 0 citations
The emerging era of quantum computing has highlighted the importance of secure communication systems that combine quantum-resistant cryptography, quantum communication, and advanced security analytics. This systematic review critically examines hybrid models of quantum and classical artificial intelligence, focusing on architectures for quantum key distribution (QKD), intrusion detection, network management, and the integration of post-quantum cryptography. Following PRISMA 2020 guidelines, studies from January 2020 to July 2026 were sourced from IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, Wiley Online Library, and backward citation searches. Out of these, 30 primary studies met the inclusion criteria and were evaluated using an eight-item quality rubric and a five-level evidence-maturity framework. The findings are grouped into three categories: AI-enhanced QKD and secure communication, hybrid quantum–classical learning for intrusion detection, and post-quantum or hybrid cryptographic solutions. AI-supported QKD research has shown notable reductions in parameter search time while maintaining near-optimal secret-key rates, primarily in simulation settings. Hybrid quantum machine learning models showed competitive intrusion detection accuracy and F1 scores, but improvements over classical methods were modest, dataset-dependent, and often lacked comprehensive reporting on false positives, statistical significance, or computational costs. Post-quantum cryptography approaches demonstrated greater maturity, with evaluations involving Transport Layer Security (TLS), Internet Protocol Security (IPsec), embedded devices, wireless links, and hardware accelerators, although performance varied with platform resources, cryptographic object sizes, network conditions, and side-channel protections. Among the studies, half were simulation-based, 30% involved laboratory experiments, and 20% were functional prototypes; none showed sustained operational deployment of an integrated hybrid quantum–classical AI security system. The review concludes that current evidence supports application-specific feasibility rather than universal quantum advantage. Achieving practical deployment requires standardized benchmarking, comparable classical baselines, transparent cost analysis, hardware validation, interoperable protocols, and longer-term field testing.
Kyiewu Bernard, A. Clinton, Odoi Henry et al.· Journal of Electrical System...· 0 citations