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Journal of Technologies Information and Communication

Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions
Forhad Hossain 1 * , Kamrul Hasan 2, Al Amin 1, Shakik Mahmud 3
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1 St. Francis College, Brooklyn, New York, United States
2 Trine University, Indiana, United States
3 College Para, Jaldhaka-5330, Nilphamari, Bangladesh
* Corresponding Author
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Research Article

Journal of Technologies Information and Communication, 2024 - Volume 4 Issue 1, Article No: 32222
https://doi.org/10.55267/rtic/15824

Published Online: 30 Dec 2024

Views: 1922 | Downloads: 1319

Open Access
How to cite this article
APA 6th edition
In-text citation: (Hossain et al., 2024)
Reference: Hossain, F., Hasan, K., Amin, A., & Mahmud, S. (2024). Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication, 4(1), 32222. https://doi.org/10.55267/rtic/15824
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Hossain F, Hasan K, Amin A, Mahmud S. Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication. 2024;4(1):32222. https://doi.org/10.55267/rtic/15824
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Hossain F, Hasan K, Amin A, Mahmud S. Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication. 2024;4(1), 32222. https://doi.org/10.55267/rtic/15824
Chicago
In-text citation: (Hossain et al., 2024)
Reference: Hossain, Forhad, Kamrul Hasan, Al Amin, and Shakik Mahmud. "Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions". Journal of Technologies Information and Communication 2024 4 no. 1 (2024): 32222. https://doi.org/10.55267/rtic/15824
Harvard
In-text citation: (Hossain et al., 2024)
Reference: Hossain, F., Hasan, K., Amin, A., and Mahmud, S. (2024). Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication, 4(1), 32222. https://doi.org/10.55267/rtic/15824
MLA
In-text citation: (Hossain et al., 2024)
Reference: Hossain, Forhad et al. "Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions". Journal of Technologies Information and Communication, vol. 4, no. 1, 2024, 32222. https://doi.org/10.55267/rtic/15824
ABSTRACT
The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and machine learning to enhance cybersecurity across multiple dimensions. The research employs a structured methodology, beginning with the integration of Quantum Key Distribution (QKD) for secure key exchange and progressing through the deployment of Quantum Neural Networks (QNN) and Quantum Support Vector Machines (QSVM) for anomaly detection and adversarial threat management. The framework also incorporates Quantum Reinforcement Learning (QRL) for autonomous incident response, a Quantum Authentication module for securing identity verification using biometric and behavioral data, and a Policy Compliance Interface powered by Quantum Compliance Analyzers for regulatory adherence. Experimental results demonstrated substantial improvements in cybersecurity metrics, including a 96% accuracy in threat detection, a 28% reduction in incident response time, and a 96% success rate in compliance simulations. These findings underscore the framework's capacity to offer adaptive, scalable, and efficient cybersecurity solutions tailored to modern challenges. This study provides a significant step toward integrating quantum technologies into practical cybersecurity applications, paving the way for future innovations in intelligent, secure, and adaptable defense systems.
KEYWORDS
Quantum Machine Learning Cybersecurity Quantum Neural Networks Cyber Threats Quantum Key Distribution
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