## 3 Key Technologies of UAV-Assisted SAGINs
### Security Control
#### 3.2.2 Safety Control Technologies  
The current methods for addressing the five major attack strategies mentioned above are summarized into the following three categories: encryption and authentication, intrusion detection and prevention, and physical-layer security. The relationships among these categories are illustrated in Figure 4.  
1. _Encryption and Authentication_: Encryption algorithms can protect data from being eavesdropped or tampered with during transmission, but due to the limited computing resources of UAVs, encryption algorithms should be lightweight and low-energy. In [59], the authors proposed a lightweight authentication and key agreement protocol based on physically unclonable functions (PUFs), which generates a unique identifier for the UAV through PUFs, achieving identity authentication, access control, and intrusion detection. While ensuring security, it reduces computational and communication overheads. In [60], the authors developed an encryption scheme based on discrete wavelet transform (DWT) and chaos theory, specifically for data protection in real-time applications in UAVs. This approach optimizes computational efficiency by decomposing the frequency components of the data while improving data security by using random noise diffusion. This scheme can effectively balance encryption strength and computational cost and is suitable for UAVs with limited battery capacity. Blockchain has developed into a general technology that provides higher security and transparency for UAV communications with its decentralized distributed storage, smart contracts, and tamper-proof features. According to our investigation, blockchain and UAV communication technologies can be effectively combined. In the reference [61], [PERSON] et al. focused on the blockchain framework Hyperledger Fabric, which is a platform that allows developers to develop private and permitted blockchains. The authors showed how to use different features of Hyperledger Fabric to protect drone swarm networks, including using digital certificates for identity management and using chain codes to define assets and transaction rules. In the reference [62], [PERSON] et al. proposed a trust management framework based on blockchain timestamp sequences, which reflects the performance of UAVs through the observation of distributed observers. It can also detect damaged distributed observers and reduce their impact on drone evaluation. In [27], the authors proposed a blockchain-based drone-assisted cellular network-secure spectrum trading framework, which uses consortium blockchain technology to achieve distributed secure spectrum trading through the assistance of mobile edge computing. And, they designed a low-complexity non-uniform pricing algorithm and a distributed uniform pricing negotiation algorithm to maximize the profits of network operators and drone operators. 2. _Intrusion Detection and Prevention:_ Intrusion detection and defense in UAV communications are crucial. Under conditions such as highly dynamic network environments, multi-node collaboration, and resource constraints, a balance between real-time and lightweight processes must be achieved. The collaborative mechanism under a distributed architecture is particularly critical to ensuring the overall security of the network. At the same time, it is necessary to use artificial intelligence and edge computing technologies to achieve efficient threat detection and rapid response to effectively respond to various security threats such as data eavesdropping, navigation deception, and denial-of-service attacks. Traditional intrusion detection methods rely on static rules and feature matching, which find it difficult to cope with complex and changing network environments and new attacks. The introduction of machine learning and deep learning technologies has enabled intrusion detection to shift from a static mode to a dynamic mode, greatly improving the accuracy and efficiency of detection.