## 3 Key Technologies of UAV-Assisted SAGINs
### Security Control
#### 3.2.2 Safety Control Technologies
In [63], the authors discussed in detail an intrusion detection system designed using DRL methods, which showed high efficiency and robustness in detecting unknown threats in dynamic aerial computing networks. In [64], [PERSON] et al. designed a feature extraction method based on autoencoders, combined with machine learning models for intrusion detection in drone communications. Experiments on actual data sets showed that this method was superior to traditional methods in both binary and multi-classification tasks. In [65], [PERSON] et al. proposed an intrusion detection system based on deep convolutional neural networks, focusing on solving the problem of malicious behavior identification in the case of limited computing power in drone networks. Its implementation proved that the system can process large amounts of traffic data in complex network environments and maintain a low false alarm rate. Distributed intrusion detection systems (DIDSs) can defend against potential malicious attacks through the collaboration of multiple drones. By combining DIDSswith deep learning, potential threats can be detected and classified in real time during drone communications. [PERSON] et al. [74] proposed a real-time collaborative intrusion detection system that uses deep learning technology to detect complex threats such as zero-day attacks through the collaborative operation of multiple drone nodes. Experiments showed that the system significantly improves detection efficiency and reduces the risk of single point failures. In [66], the authors proposed a collaborative intrusion detection system (CIDS) based on Hyperledger Fabric and Snort IDS and studied how blockchain technology can enhance the robustness and efficiency of the CIDS through trust management. In addition, for DDoS attack detection, [PERSON] et al. [67] proposed a smart-grid DDoS attack tracking scheme that uses UAV mobility and an adaptive beam search algorithm to reconstruct attack paths and accurately locate attack sources. 3. _Physical-Layer Security_: In physical-layer security, attackers often use frequency band interference or GPS spoofing. To protect UAV communications from interference, the anti-interference ability of communication links can be improved through frequency hopping, beam-forming, and waveform encryption. In [26], [PERSON] et al. proposed a method to achieve secure communication using a hybrid free space optical (FSO)/wireless RF link in two-stage uplink transmission. By using different communication links in two stages, attacks from RF eavesdroppers can be effectively defended against to maximize confidentiality. In [68], the authors proposed a double-delayed deep deterministic policy gradient (TD3) algorithm based on DRL, which solves the dual goals of energy efficiency and communication fairness in UAV-assisted air-ground integrated networks under interference attacks. In particular, it solves how to optimize system performance through intelligent resource scheduling when channel characteristics change randomly and easily cause channel capacity imbalance. In addition, reconfigurable intelligent surfaces (RISs) are able to precisely control the propagation direction, phase, and amplitude of electromagnetic waves, maximizing the signal-to-noise ratio and mitigating the impact of interference signals. Additionally, by adjusting the reflection paths to optimize signal transmission, it makes the signals difficult to track. [PERSON] et al. [28] proposed a RIS-assisted satellite-terrestrial hybrid relay network with a joint beam-forming design to address the blockage of satellite-to-user links. Using an alternating optimization algorithm, the design reduces the transmission power of both satellites and base stations while meeting user rate requirements. Subsequently, based on these results, [PERSON] et al. further optimized the beam-forming weights and reflection coefficient matrix using a penalty function and double-decomposition algorithm to maximize the achievable secrecy rate of the ground station [70]. For GPS fraud, [PERSON] et al.