Next-Gen Data Analytics and Intelligent Automation
AI Powered Post-Quantum Cryptography: Strengthening U.S. Cybersecurity with Quantum Computing
Authors: Mustakim Bin Aziz, Mani Prabha, Sweety Rani Dhar, Md Samiun, Rukshanda Rahman, SyedaFarjana Farabi, Ali Hassan and Syeda Kamari Noor
Publishing Date: 12-04-2026
ISBN: 978-81-975670-6-3
Abstract
The rise of quantum computing threatens traditional cryptographic methods like RSA and ECC, which are vulnerable to quantum algorithms such as Shor’s and Grover’s. This study aims to assess cybersecurity vulnerabilities and enhance cyber threat detection using machine learning and deep learning techniques. The NSL-KDD dataset is employed for intrusion detection, utilizing feature selection methods like recursive feature elimination and mutual information analysis. This study proposed IntruDualNet which is Dual Output based deep learning model where it’s predicted both binary and multiclass classification. Experimental results show high detection accuracy, with 99.70% for binary classification and 99.49% for multiclass classification, effectively identifying threats like DDoS, SQL injection, and XSS. The findings highlight the urgency of transitioning to post-quantum cryptographic standards and integrating AI-driven security solutions to mitigate emerging threats.
Keywords
IntruDualNet, Post-Quantum Cryptography, Cybersecurity, Quantum Computing, Intrusion Detection, Machine Learning.
Cite as
Mustakim Bin Aziz, Mani Prabha, Sweety Rani Dhar, Md Samiun, Rukshanda Rahman, SyedaFarjana Farabi, Ali Hassan and Syeda Kamari Noor, "AI Powered Post-Quantum Cryptography: Strengthening U.S. Cybersecurity with Quantum Computing", In: Kusum Kumari Bharti and Noor Firdoos Jahan (eds), Next-Gen Data Analytics and Intelligent Automation, SCRS, India, 2026, pp. 63-74. https://doi.org/10.56155/978-81-975670-6-3-6