# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### UAV Self-Organizing Network Communications Security  
UAV SON communication treats UAVs as nodes to achieve autonomous networking, dynamic routing, and data transmission between nodes through wireless communication technology. A representative structure of a UAV SON is presented in Figure 17. In networking, UAV communication protocols aim to support secure and reliable communication between UAVs, ground control stations, and other UAVs. However, the open nature of UAV wireless channels makes them susceptible to security threats such as eavesdropping, tampering, and DoS attacks. In addition, UAVs typically operate under constrained resources and require lightweight, real-time, and highly secure communication protocols to meet their special needs.  
In 2020, [PERSON] et al. proposed a self-organized mapping technique that enables UAV arrays to dynamically adjust their topology according to users' demands and form a hierarchical network, improving the efficiency and security of UAV SON [186]. In 2024, [PERSON] et al. proposed a secure communication scheme for UAV FANETs, which includes three phases: UAV registration, UAV and ground station authentication, and inter-UAV authentication [187]. In disaster response and environmental monitoring, [PERSON] et al. deployed a dynamic communication network using AI-driven UAVs to optimize resource allocation, collect and analyze data, and realize dynamic decision-making [188]. With respect to routing protocols, secure strategies have been explored to protect UAV cluster networks. [PERSON] et al. [189] proposed a secure routing protocol named PASER to address UAV routing attack challenges in wireless multihop networks (WMNs).  
However, traditional methods often encounter issues with limited bandwidth, especially when multiple UAVs transmit data at the same time. UAVs often experience dynamic topology changes, but most existing papers are based on ideal environments [190; 191], leading to an increase in the routing interruption rate. For diverse and extreme environments such as urban, mountainous, and marine environments, traditional AI methods exhibit weak generalizability [190; 192] and lack cross-scene migration capabilities.  
However, LLMs offer potential solutions for optimizing the topological relationships and bandwidth distribution of UAV networks via optimization algorithms such as the improved NSGA-II algorithm. These models can be combined with virtual reality simulation technology for multimodal data fusion [191], achieving a smooth transition from simulation to real scenarios. When combined with the LLM-based migration learning pretraining cross-scenario generic policy [192], the routing disruption rate in extreme environments,  
Figure 17: UAV SON structure.  
such as heavy rain, is reduced. In addition, LLMs can predict link quality by capturing long-distance dependencies [193; 194], improving the accuracy of routing prediction and further reducing the routing disruption rate. With adaptive mesh techniques, LLMs can be applied to generate adaptive routing table update strategies [194] on the basis of real-time physical characteristics, such as airflow velocity and pressure changes during flight.