A Machine Learning-Based Framework for Enhancing Security in Post-Quantum Cryptography

• Author(s): Abimbola Basiru Owolabi; Wumi Ajayi
• Paper ID: 1717936
• Page: 3321-3328
• Published Date: 22-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

The rapid advancement of quantum computing presents a major threat to conventional public-key cryptographic systems, thereby accelerating the global adoption of Post-Quantum Cryptography (PQC) standards approved by the National Institute of Standards and Technology (NIST). Although PQC algorithms are specifically designed to resist quantum-based attacks, they remain vulnerable to implementation-level weaknesses such as side-channel leakage, fault injection attacks, and machine-learning-assisted cryptanalysis. This research investigates the emerging role of Quantum Machine Learning (QML) in strengthening and evaluating the security of PQC systems. The study explores the dual application of QML as both an offensive and defensive cybersecurity mechanism. On the offensive side, quantum-enhanced learning models, including variational quantum classifiers and quantum kernel-based approaches, are analysed for their capability to accelerate side-channel analysis and cryptanalytic attacks by learning hidden leakage patterns from power consumption traces, electromagnetic emissions, and timing information more efficiently than conventional machine learning models. On the defensive side, the research proposes QML-driven countermeasures such as quantum generative models for creating randomized noise distributions that obscure implementation leakage and reinforcement learning agents capable of dynamically adapting cryptographic masking techniques in real time. Experimental implementation will be conducted using IBM Quantum hardware alongside PQC algorithms including CRYSTALS-Kyber and CRYSTALS-Dilithium. The research will evaluate the comparative performance of quantum-assisted attack and defence mechanisms with respect to detection efficiency, leakage resilience, and computational overhead. Expected outcomes include the development of open-source QML-based cybersecurity toolkits, comprehensive security evaluation frameworks for PQC migration strategies, and the establishment of a “quantum-secure-by-design” approach for intelligent cryptographic systems. The study further supports global efforts toward quantum-safe infrastructure development and promotes collaborative cybersecurity research initiatives between the United States and Nigeria.

Keywords

Post-Quantum Cryptography, Quantum Machine Learning, Quantum Security, Side-Channel Analysis, NIST PQC, Kyber, Dilithium, Cybersecurity

Citations

IRE Journals:
Abimbola Basiru Owolabi, Wumi Ajayi "A Machine Learning-Based Framework for Enhancing Security in Post-Quantum Cryptography" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 3321-3328

IEEE:
Abimbola Basiru Owolabi, Wumi Ajayi "A Machine Learning-Based Framework for Enhancing Security in Post-Quantum Cryptography" Iconic Research And Engineering Journals, 9(11)