The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor’s algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system’s anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing.
Authors: Sheetal Temara, L. Bhagyalakshmi, Sanjay Kumar Suman, M. Shakunthala, N. Thulasi Chitra, Kishor Golla
DOI: https://doi.org/10.1109/iccece61355.2025.10940245
Publish Year: 2025
