AI-Driven Security Evaluation of the ZUC Stream Cipher in 5G Networks

The ZUC stream cipher plays a critical role in safeguarding confidentiality and integrity in 5G AKA protocols. As machine learning (ML) and artificial intelligence (AI) emerge as new tools in cryptanalysis, assessing cipher resilience against such techniques has become essential. This paper presents a comprehensive evaluation of ZUC’s robustness against ML-Based attacks using keystream instances generated through real-world 5G testing infrastructure. Different attack strategies are implemented, including bitwise machine learning prediction, entropy and randomness analysis, Variational Autoencoder (VAE)-based reconstruction, protocol metadata fusion, reinforcement learning-based refinement, and statistical correlation modeling. Our results demonstrate that despite extensive AI-based cryptanalysis efforts, ZUC maintains a high security margin, prediction accuracies from baseline models remain below 58.03%, a metric that establishes the cipher’s resilience rather than indicating a practical weakness. Entropy analysis further confirms near-ideal randomness. Minor bit position variations and metadata correlations were observed, which did not significantly impact overall security. These findings establish ZUC’s strong resistance against contemporary AI-based threats and offer critical insights for designing future AI-resilient cryptographic standards in 5G and beyond.