# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Trust Evidence Data Cleaning  
To assess the security of satellite network nodes, it is necessary to collect interaction information among the nodes. For internal attacks on the satellite network, the most  
Figure 1: Satellite network routing attack architecture. Figure 2: Satellite network security trust model. commonly used credible evidence is the number of successful communication instances and the number of communication failures between nodes. Due to the unique environment of satellite networks, it is not suitable to utilize a centralized trusted authority to compute the trust values of satellite nodes. Consequently, this paper employs a distributed collaborative approach among satellite nodes to calculate their trust values. When a satellite node evaluates the trustworthiness of its neighboring forwarding satellite nodes, it needs to track their historical packet forwarding behavior. Therefore, to track the historical forwarding behavior of the evaluated satellite node, this paper introduces a two-hop acknowledgment mechanism [18]. The mechanism operates as follows: If the evaluating node receives acknowledgment messages from both its neighbors and the neighbors of its neighbors within a specified timeframe, it indicates that the evaluated neighboring node has successfully forwarded the packet. If, within the specified timeframe, the evaluating node neither receives acknowledgment messages from its neighbors nor from the two-hop neighbors, it implies that the evaluated neighboring node explicitly refused to forward the packet. If the evaluating node within the specified timeframe only receives acknowledgment messages from its neighbors but not from the two-hop neighbors, it cannot determine whether the neighboring node successfully forwarded the packet. Therefore, based on the utilization of the two-hop acknowledgment mechanism, this paper initially defines the following formula:  
\[a=\frac{s}{s+f+u} \tag{1}\]  
\[b=\frac{f}{s+f+u} \tag{2}\]  
\[c=\frac{u}{s+f+u} \tag{3}\]  
where \(s\) represents the count of successful forwarding, \(f\) represents the count of failed forwarding, and \(u\) represents the count of cases where successful forwarding is uncertain. Additionally, \(a\) stands for the success forwarding rate, \(b\) stands for the failure forwarding rate, and \(c\) stands for uncertainty. To alleviate the influence of satellite networks due to factors such as electromagnetic radiation and cybersecurity, the D-S evidence theory is used to correct the above trust evidence. Given a recognition framework \(\Omega=\{T,\sim T\}\), where \(T\) means trust, \(\sim T\) means dislike, \(m\{T\}\) represents the probability ratio of successful communication, \(m\{\sim T\}\) represents the probability ratio of failed communication, and \(m\{T,\sim T\}\) means the probability ratio of uncertain environmental factors, the calculations of the three can be calculated based on the above Formulas (1)-(3). The definition of a direct trust vector is \(DR=\{m\{T\},m\{\sim T\},m\{T,\sim T\}\,\). In the beginning, because the nodes in the satellite network have not had interaction with other nodes, the result of the \(DR\) here is \(\{0,0,1\}\). The three components of the vector, respectively, represent the probability ratio of trust, the probability ratio of distrust, and the probability ratio of uncertainty. Here, 1 indicates that no communication evidence has been collected at the beginning, and it is impossible to determine whether the satellite node should be trusted. The trust model of this article adopts a periodic update method, so the node update cycle can be set to \(\Delta t\). If the current \(DR_{ij}\) (\(t_{n}\)) passes after \(\Delta t\), it will change to \(DR_{ij}\) (\(t_{n+1}\)). \(DR_{ij}\) represents the direct trust vector, and \(t_{n}\) represents the current time point.