# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 2 Related Research
The drawback of this algorithm is that constructing multiple secure paths is not only time-consuming but also consumes a significant amount of node storage resources. Reference [25] proposed a space information network secure routing protocol based on intrusion detection. To enhance the accuracy of attack detection, this technique employs multiple nodes to identify malicious behavior, thus establishing a collaborative intrusion detection system. Additionally, this approach introduces a trust management system, which, by integrating intrusion detection and trust management, enables more precise identification of malicious nodes, thereby enhancing the security of satellite node routing. The advantage of this technique lies in its use of proactive intrusion detection, which offers heightened sensitivity and accuracy in detecting malicious behavior. However, its drawback is that the application of collaborative intrusion detection technology consumes a significant amount of satellite resources. Reference [26] proposed that in satellite networks, the trust measurement mechanism and routing selection strategy work effectively when the network load is balanced and light. However, uneven network load distribution is an inherent characteristic of satellite networks. Once the load increases, nodes with higher trust levels may forward an increasing number of packets, leading to congestion in certain nodes and degrading network performance. To address this, the paper introduced load-balancing techniques to enhance the trust management system. This enabled the satellite network to consider both network load and security simultaneously. However, a drawback of this approach is that the trust management system employed a simple mathematical model, resulting in lower accuracy in identifying malicious nodes. Reference [27] proposed a trust management model based on generative adversarial network (GAN) in an underwater sensor environment. This approach combined artificial intelligence and trust management models to effectively detect malicious behavior. Additionally, the paper considered the energy consumption of underwater sensors, achieving a balance between energy and security during the routing process. The advantage of this method lies in its utilization of an intelligent security trust model, providing strong assurance for the routing security of underwater sensors; however, its drawback is the relatively high resource consumption. The anomaly detection module in the field of deep learning has a very good effect on the detection of malicious nodes, so many scholars have researched it. Reference [28] used the combination of the generative adversarial network (GAN), VAE network, and long short-term memory (LSTM) model to solve the problem that it takes a long time to find the optimal mapping from real-time space to potential space in the anomaly detection stage. To improve the performance of security monitoring, ref. [29] proposed a two-stage algorithm (S2-VAE) stacked fully connected variational autoencoder network model, which obtained excellent detection results on four public datasets. To solve the problem of poor interpretability of anomaly detection methods in satellite remote sensing test data, ref. [30] proposed an anomaly detection framework using causal network and feature-attention-based long short-term memory (CN-FA-LSTM). The proposed method is more interpretable than other commonly used prediction models, and its general applicability was verified on two common datasets. It was proposed in [31] that errors in satellite remote sensing test data would lead to false anomalies. To solve this problem, the deviation divide mean over neighbors (DDMN) was used in this study to model multivariate time series data by using a long short-term memory network, effectively avoiding false positives. It is necessary to study the development of the ant colony algorithm because the ant colony algorithm is used in the basic routing in this paper.