# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### Lessons Learned  
Section 4 presents a comprehensive investigation of the significant advances in AI technologies in the field of UAV security applications while also discussing the serious limitations of traditional AI approaches. Table 5 summarizes key research advances across different security areas and categorizes AI methods. Traditional AI methods, such as ML and DL, are primarily effective in relatively stable and well-defined UAV security scenarios. These methods contribute to enhanced sensing accuracy by analyzing data from physical sensors, improving radio anti-interference ability, and increasing the accuracy of UAV identification. Additionally, they support the protection of UAV confidentiality and communication security within the SON. These studies highlight the potential of AI technology to improve UAV security while also highlighting the limitations of current methods in terms of dataset size, model generalizability, and the difficulty of fusing heterogeneous data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Table 6 summarizes the limitations of traditional AI models in the field of UAV security. Sensing security still relies too much on physical sensors, and multisensor data are heterogeneous and difficult to fuse. In radio anti-interference, traditional methods have poor generalizability and lack security in the case of new types of attacks. In the field of UAV identification, the problem of maintaining accuracy in complex and changing dynamic environments is more prominent. In terms of cryptography, there is a lack of agreed-upon protocols or standards. UAV SON security relies on sensitive datasets, and it is difficult to guarantee that private data will not be leaked. These limitations indicate that although traditional AI models are well-suited for basic UAV security tasks, they fall short in more complex and dynamic UAV security requirements. To mitigate the limitations of traditional ML and DL models, LLMs offer promising application potential in UAV security. They are designed to process and analyze heterogeneous data from multiple sources, enabling more comprehensive security threat detection and response. By leveraging their ability to learn from large and diverse datasets, LLMs can adapt to new attacks and provide robust solutions. Additionally, LLMs exhibit real-time decision-making capabilities and employ adaptive security measures, making them ideal for advanced UAV security applications where traditional AI falls short. To be more specific, on the software side, edge--cloud collaboration is implemented using lightweight frameworks such as TensorFlow Lite and PyTorch Mobile for local data processing, minimizing latency and dependence on cloud resources [203], whereas a real-time operating system optimizes resource management, improves reliability, and reduces the risk of interference [204]. For hardware deployment, sensor fusion with integrated cameras, using LIDAR and millimeter wave radar, provides multimodal environment sensing [155], while embedded GPUs accelerate AI inference to meet real-time demands [205]. Secure communications via 5G and satellite modules enable encrypted data transfer and multi-drone coordination, while a hybrid network architecture balances local edge processing with cloud-based LLMs [206]. LLMs process sensor data through optimized edge frameworks or API-driven cloud interactions, leveraging hardware accelerators like NVIDIA Jetson GPUs for real-time performance. Unlike traditional DL models, which are less adaptable to complex scenarios, LLMs enable dynamic decision-making and natural language processing for enhanced security [155]. Additionally, open-source flight control systems,