Using Machine Learning to Enhance PostQuantum Cryptographic Algorithms
MmaduekweEbuka Paul, Femi Osholake Femi Osholake, Je ersonEderhion Je ersonEderhion, Tolu-iloriIyanuoluwa Tolu-iloriIyanuoluwa
Abstract
The incoming need for defense against quantum computer attacks has motivated researchers to prioritize post-quantum cryptography (PQC) because this approach develops encryption which quantum computers cannot break. A great number of PQC algorithms create complex challenges regarding both computational requirements and key length as well as potential security weaknesses that require optimization for actual implementation. ML technology provides an approach to enhance the performance of post-quantum cryptography systems while securing their operations and making them more adaptable. The key generation process becomes more efficient while parameter selection reaches optimized results thanks to ML techniques which strengthen attack resilience through implementation protection against anomalies and side-channel attacks. The paper explores the ML and PQC connection while presenting potential ML applications that enhance the effectiveness of PQC methodologies including lattice-based schemes hash-based schemes and multivariate-quadratic and code-based approaches. The research document outlines integration problems and prospective directions between PQC and ML which incorporate considerations for security attacks and operational and computational constraints. Through their union PQC and ML enable the creation of additional secure cryptographic technologies that provide efficient scaling potential to overcome quantum computing threats.