# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Intelligent Security Trust Model  
The variational autoencoder is an unsupervised model. Although its structure is similar to the self-coder, the principle is very different. The potential vector obtained by VAE is determined, and it cannot generate vectors that differ from the original input, so its generation process can be regarded as a discrete situation, while the encoder of variational autoencoder learns the distribution of input data, including mean and variance, and in this process, the noise of normal distribution can be added to enhance the uncertainty of the system. And because it learns the distribution of the data, the intermediate variables are in a continuous state, so there can be small changes when generating the output. Because of this, reconfiguration probability can be used to calculate outliers during anomaly detection, which is more conducive to the accuracy of results. The principle of the variational autoencoder model framework with attention mechanism can be divided into the following processes: first, multiple intermediate continuous potential vectors are generated in the coding process; secondly, by introducing the attention mechanism to record the temporal relationships of different intermediate vectors, the VAE network can perform a weighted summation of intermediate latent variables to obtain the final latent variable. Among them, the vector that records the correlation between time is \(q_{c}\). The weighted product of the \(q_{c}\) vector and multiple intermediate vectors can obtain the potential vector with the time-important information. Then, the final potential vector is input into the decoder for training to obtain the final intelligent security trust model. In a word, the method extracts the time importance information of the original sample, and the detection accuracy is improved compared with the VAE network without the attention mechanism. The trust vectors have been cleaned by D-S evidence theory to be \(T_{c}=\{l_{1},l_{2},l_{3}\cdots l_{w}\}\), which has \(w\) dimensional, where the component of the trust vector represents the comprehensive trust value of the continuous different moments. Multiple historical trust vectors can be used in Figure 3 and fed to the encoder of the VAE module, which can reduce their dimensions to generate multiple compressed potential vectors that record the characteristic information of the original trust vector. To capture the temporal relationship between trust vectors using the attention mechanism, the encoder can generate multiple intermediate potential variables after processing the trust evidence, that is:  
\[g(x_{i})=\{p_{1},p_{2},\cdots,p_{n}\} \tag{8}\]  
where \(p_{j}(j=1,2,\cdots,n)\) are the compressed vectors obtained by the encoder. Assume that the query in the attention mechanism follows the standard Gauss distribution, which can generate the final potential variable by extending the attention mechanism into an intermediate potential variable. This process is divided into three steps. In the first step, we calculate the cosine similarity of \(q_{c}\) and \(p_{j}\):  
\[S_{j}=\frac{q_{c}\times p_{j}}{\left|\left|q_{c}\right|\right|\times\left| \left|p_{j}\right|\right|} \tag{9}\]  
Due to the limited resources in satellite networks, this paper employs the 2-norm to calculate the values of \(||q_{c}||\) and \(\left|\left|p_{j}\right|\right|\) in Equation (9). The calculation formula is shown as follows:  
\[||y||=\left(\left|y_{1}\right|^{2}+\ldots+\left|y_{n}\right|^{2}\right)^{\frac {1}{2}} \tag{10}\]  
In the second step, the required weight of the attention mechanism can be calculated as follows:  
\[h_{j}=\frac{\exp\left(S_{j}\right)}{\sum\limits_{j=1}^{n}\exp(S_{j})} \tag{11}\]  
In the third step, after obtaining and corresponding alignment rights, the final potential variables \(z\) can be obtained as follows:  
\[z=\sum\limits_{j=1}^{n}h_{j}\times p_{j} \tag{12}\]  
Figure 3: VAE abnormal detection model.