# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### Lessons Learned
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field** & **Ref.** & **Contributor** & **Year** & **Methods** & **Main Point** \\ \hline \multirow{8}{*}{**Cryptography**} & \multirow{2}{*}{[183]} & [PERSON] & \multirow{2}{*}{2024} & \multirow{2}{*}{ML} & A UAV data security enhancement framework; achieves a significant reduction in data tampering risks and efficient data validation cycles. \\ \cline{3-5}  & & & University & & \\ \cline{2-5}  & & King Abdulazie & \multirow{2}{*}{2024} & \multirow{2}{*}{DL} & A hybrid DL-based arithmetic optimization algorithm; achieves high accuracy in threat detection and efficient performance in securing communications. \\ \cline{3-5}  & & & & & A federated learning framework for LLMs using encryption and model splitting achieves secure training with comparable performance to that of centralized models and robust defense against gradient-based attacks. \\ \hline \multirow{8}{*}{**Self-Organizing Networks**} & \multirow{2}{*}{[201]} & Beijing University & \multirow{2}{*}{2024} & \multirow{2}{*}{ML} & A topology-aware resilient routing protocol using adaptive Q-learning; achieves a 25.23% lower overhead, a 9.41% higher packet delivery rate (PDR), and a 5.12% lower energy consumption compared to existing methods. \\ \cline{3-5}  & & & & & An optimal AODV-based routing algorithm; achieves a successful packet delivery rate of above 80%. \\ \cline{3-5}  & & & & & A UAV networking security framework using generative AI and DMs; achieves a 25% enhancement in classification accuracy for anomaly detection and robust privacy preservation, with over 10% higher communication rates. \\ \cline{3-5}  & & & & & A LIM-Dusta framework; achieves near-perfect accuracy in converting free-next raw requests into structured UAV operation tasks, significantly enhancing operational efficiency and adaptability in uncertain environments. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._such as ArduPilot and GymFC, are integrated with AI frameworks to enable autonomous functionality, augmented by security measures [155]. In this context, the development of LLMs provides new opportunities in the field of UAV security, as shown in Figure 18. Owing to their powerful data processing and intelligent decision-making abilities, LLMs can effectively respond to complex security problems in UAV communication, particularly in dynamic and unpredictable scenarios. For example, in environments with fluctuating network conditions, such as urban areas with dense signal interference, remote regions with sparse connectivity, or contested airspace with active jamming, LLMs leverage their extraordinary learning capabilities to achieve a more accurate identity authentication mechanism, resisting forgery and counterfeiting attacks by analyzing real-time communication patterns and adapting to evolving threats. Similarly, using their ability to analyze signal characteristics, LLMs can effectively identify and counteract radio interference in scenarios such as high-traffic air corridors or electronic warfare zones, ensuring communication link stability despite rapid signal noise or jamming intensity changes. LLMs can also facilitate the optimization of communication protocols to enhance the security and reliability of UAV SON, adapting to shifting topologies or sudden node failures, such as those caused by weather disruptions or hardware malfunctions. In addition, when combined with cryptography techniques, LLMs can strengthen encryption algorithms to resist potential threats from emerging technologies, ensuring robust protection, even in future-facing, technologically volatile contexts.