# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 2 Related Research  
D-S evidence theory has been widely discussed and used in recent years because it can combine uncertain information from different sources with different levels of abstraction. However, due to the problem of evidence conflict in this theory, many scholars have conducted relevant research on it. Reference [10] improved on the traditional D-S evidence theory by reassigning weight factors before evidence fusion to solve the problem of counterintuitive results when D-S evidence theory fuses highly conflicting information. Reference [11] proposed a new method for measuring global uncertainty, which not only retains the advantages of previous measurements but also has higher sensitivity and wider scope to changes in evidence. Reference [12] analyzed the relationship between D-S evidence theory and classical probability theory and then proposed a generalized evidence combination formula. The advantage of this method is that it can alleviate the requirement of evidence independence and make D-S evidence theory perform better in practical application. In addition, ref. [13] applied the D-S evidence theory to the sensor system of unmanned vehicles, so that unmanned vehicles could make better decisions in actual scenarios. Since satellite nodes are exposed to an open space environment, satellite network routing protocols are inevitably subject to many attacks [14], which can be roughly divided into two types: although the encryption mechanism can effectively protect against external attacks, it cannot deal with the internal attacks of the satellite network. Therefore, a security protection method against internal attacks has been put forward by the people--trust management mechanism. With the development of malicious attack mode, the structure of the trust model has also risen from the simple weighted principle to the construction of various complex mathematical models. Reference [15] adopted a simple mathematical trust model, and its trust management model was based on the weighted average of trust values. In this paper, direct trust values, indirect trust values, and comprehensive trust values were established by using simple mathematical formulas for the interaction behaviors between satellite network nodes and neighbors, to determine the security of satellite network nodes. The advantage of this scheme is that it makes full use of the dynamic characteristics of the satellite and adopts adaptive adjustment strategies to adjust the trust value to detect malicious behaviors and respond quickly when the network fails. However, the trust evaluation model of this scheme uses the weighted average method to calculate the comprehensive trust value, so the evaluation of trust value is highly subjective and the evaluation results are not accurate. Reference [16] discussed the inference model based on the Bayesian network, which uses a set of current observations (i.e., direct experience) in Bayesian theory to predict the future state of the route, and its nodes estimate the parameters of the prior distribution through the collected recommendation information and combine the direct interaction to obtain a posterior distribution, based on which the trust value of each interval can be predicted. Since this scheme uses Bayesian theory to deduce the solution of trust value, its accuracy in the security assessment of nodes is greatly improved. However, the adoption of Bayesian theory requires the provision of prior probability and conditional probability, which may not be fully provided in the establishment of the security trust model. Reference [17] proposed a fuzzy trust model based on experience and rationality, which accesses the accuracy and integrity of messages through fuzzy logic. It uses ID authentication to evaluate whether a message is authorized and uses the relevant data stored in the nearest node to measure the accuracy of the message.