A Systematic Review of Chaotic Polynomial Sequences for High-Entropy Stream Ciphers: Methods, Architectures, and Future Research Directions

Abstract

The rapid evolution of cryptographic systems in modern software engineering has intensified the need for secure, high-entropy stream ciphers capable of resisting sophisticated adversarial attacks. Chaotic systems, particularly chaotic polynomial sequences, have emerged as a promising foundation for designing lightweight and highly unpredictable cryptographic primitives due to their inherent sensitivity to initial conditions, nonlinearity, and ergodicity. This systematic review synthesizes recent advancements from 2018 to 2025 in the application of chaotic polynomial sequences for high-entropy stream cipher design, examining their mathematical foundations, architectural implementations, and integration within contemporary software engineering pipelines. The paper further explores how generative artificial intelligence techniques are increasingly intersecting with cryptographic design, enabling automated model generation, vulnerability detection, and optimization of cipher architectures. By analyzing fifty peer-reviewed studies, this review identifies prevailing trends, evaluates methodological strengths and limitations, and highlights critical research gaps in entropy maximization, hardware efficiency, and resistance to cryptanalysis. The findings emphasize the potential of hybrid approaches combining chaos theory and machine learning to redefine secure software systems. Finally, the paper proposes future research directions focusing on adaptive cryptographic frameworks, AI-assisted cipher synthesis, and secure integration into DevSecOps environments.