A Review of Vulnerabilities Analysis in Intelligent Models for Communication Radio Identification

The integration of intelligent models within the realm of communication radio identification stands as a pivotal area of current research inquiry. As the discipline of adversarial machine learning advances, the susceptibility of these models to targeted attacks has risen to prominence. Frequently, such models exhibit fragility when confronted with advanced adversarial tactics, highlighting considerable shortcomings in their inherent defensive mechanisms. The successful deployment of diverse attack methodologies accentuates these frailties, underscoring the pressing demand for enhanced robustness and fortified security protocols. Notwithstanding the paramount significance of these concerns, an observable void persists in the scholarly discourse regarding the intricate underpinnings of adversarial vulnerabilities within learning algorithms. This paper endeavors to offer an exhaustive review and analytical examination of the vulnerabilities confronting intelligent models in communication radio identification, with the objective of establishing a solid groundwork for the future enhancement of model security and resilience.