Leveraging AI-driven requirements for SysML modeling of the IoBT: A comprehensive investigation

Designing secure and adaptable systems for the Internet of Battlefield Things (IoBT) presents substantial challenges due to the complexity, variability, and high-risk operational environments inherent to modern military engagements.Deriving accurate and traceable system requirements is particularly difficult given the dynamic threat landscape and the heterogeneous nature of IoBT components.This study conducts a systematic literature review to investigate how artificial intelligence (AI), particularly Large Language Models (LLMs), can augment the derivation of cybersecurity requirements and support their structured representation through Model-Based Systems Engineering (MBSE) and Systems Modeling Language (SysML) frameworks.By synthesizing findings across current research, this work identifies key limitations in traditional manual processes.It demonstrates how AI-driven automation improves the efficiency, precision, and adaptability of security engineering in military systems.The review also highlights critical gaps in AI-to-SysML translation, traceability mechanisms, and real-time threat responsiveness.This research contributes a foundational understanding of how LLMs can be operationalized for secure systems modeling in IoBT contexts, offering a path forward for scalable, resilient, and compliant cybersecurity architectures in defense applications.