Leveraging Machine Learning for Performance Optimization in Post-Quantum Cryptographic Protocols

The advent of quantum computing presents a potential threat to conventional cryptographic systems, prompting the urgent need for post-quantum cryptography (PQC) solutions. However, the complexity of these cryptographic protocols often leads to inefficiencies in performance, hindering their widespread adoption. This paper explores the integration of machine learning (ML) techniques to optimize the performance of post-quantum cryptographic protocols. We propose an innovative framework where ML algorithms are leveraged to enhance the efficiency of key generation, encryption, decryption, and signature processes in PQC schemes. By utilizing data-driven approaches such as reinforcement learning and neural networks, we aim to reduce computational overhead and improve latency, while maintaining the robustness and security of post-quantum algorithms. Experimental results demonstrate significant performance improvements over traditional optimization methods, showcasing the potential of ML in advancing the practicality of PQC protocols. This work lays the foundation for a new class of cryptographic systems capable of withstanding quantum threats while maintaining high operational efficiency.