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Journal of Cybersecurity in AI and Quantum Computing

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Quantum Computing Threat Analysis and Cybersecurity Risk Mitigation Framework for Critical Infrastructure Protection Using Artificial Intelligence

Volume 2026, Issue 1ISSN: Pending Request (Online)Journal: Journal of Cybersecurity in AI and Quantum Computing
by 

Vugar Abdullayev ORCID profile ;

Udit Mamodiya ORCID profile ;

Mohammed Almaayah ORCID profile

Vugar Abdullayev: Department of Computer Engineering, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, Azerbaijan, AZE

Udit Mamodiya: Associate Professor & Associate Dean (Research), Poornima University, Jaipur Rajasthan, India, IND

Mohammed Almaayah: Fellowship Researcher, INTI International University, Nilai 71800, Malaysia, , MYS

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Published: 2026/09/02

Pages: 88–116

Abstract

The critical infrastructure is increasingly relying on cyber-physical systems that are interconnected and have long lifetimes and legacy cryptography, which puts them at increasing risk of future quantum attacks. This research proposes an artificial intelligence (AI)-based framework for attack detection, risk assessment for quantum attacks, and selection of feasible cybersecurity controls under operational constraints. The framework is comprised of a CNN–BiGRU temporal detector, graph-attention-based dependency analysis, deterministic cryptographic profiling, explainable risk fusion, and mixed-integer mitigation optimization. Separate train, calibration, validation, and test sets were used for water, industrial-control, and IoT applications, with the use of a reproducible multi-domain benchmark. The proposed model outperformed the conventional, temporal, and graph-only baseline models with a macro-F1 of 0.9387 and an AUROC of 0.9878 over 10 runs. Quantum exposure and dependency propagation improved high-risk prioritization without inflating attack-classification scores. Performance remained stable under moderate telemetry loss, graph perturbation, delayed threat intelligence, and alternative cryptographic profiles. The optimized mitigation plans reduced aggregate risk by 62.99–97.28%, depending on budget and downtime limits. Cross-domain transfer was strong for process-oriented environments but weaker for the network-centric IoT domain. The framework links together attack detection, the necessity of moving to post-quantum, explainable risk assessment, and mitigation with operational limitations in one process of reproducible protection for infrastructure management.

Keywords

Quantum computingCritical infrastructure protectionPost-quantum cryptographyArtificial intelligenceCybersecurity risk mitigation.

Quantum Computing Threat Analysis and Cybersecurity Risk Mitigation Framework for Critical Infrastructure Protection Using Artificial Intelligence is licensed under CC BY 4.0CCBY

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