TMD-Tradeoff and Slide Attack Analysis on the Novel ZUC-PRN Model

The rapid evolution of wireless communication technologies is epitomized by the advent of 5G-Advanced and the forthcoming 6G era. While 5G-Advanced enhances existing capabilities, 6G aims to seamlessly integrate AI-driven intelligence and sophisticated cryptographic techniques, significantly bolstering security. This paper explores the ZUC-PRN model [1], a neural network-based cipher inspired by the ZUC algorithm, proposed for enhancing wireless security in 5G and 6G networks. While ZUC-PRN shows potential for replacing traditional FSM-based ciphers, its resilience against various attacks remains unverified. We analyze its vulnerabilities to Slide and Time-Memory-Data Tradeoff (TMDT) attacks and assess its computational complexity, comparing it with the standard ZUC stream cipher. This study provides critical insights into the security and efficiency of the ZUC-PRN model, contributing to the development of more robust encryption methods for next-generation communication systems.