# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis  
This article uses NS2 simulation software(ns-allinone-2.35) to achieve simulation testing of Algorithm 1. The parameters of the satellite network are shown in Table 2:  
The basic idea of simulation is to first use Python tools to build a VAE neural network for training. As the VAE model adopted in this paper is used to detect data security at the routing level of the satellite network, the traditional ground open dataset is not suitable for use here. Therefore, the dataset needs to be made manually during the training of the VAE model, that is, the routing evidence of the ant colony algorithm is collected and processed by D-S evidence theory, and the synthetic vector is obtained and then provided to the VAE model. After that, the training results are input into NS2, and then the safe ant colony algorithm is established to find the way, and various parameters in the satellite network \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Orbital altitude (km) & 780 \\ Number of orbital planes & 6 \\ Number of satellites in orbit & 11 \\ Orbital inclination (\({}^{\circ}\)) & 86.4 \\ Orbital plane spacing (\({}^{\circ}\)) & 31.6 \\ Interstellar link latitude threshold (\({}^{\circ}\)) & 60 \\ Number of interplanetary links per satellite & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental simulation parameters. routing process are obtained by adding malicious attacks. Finally, the experimental results are exported, and the data are read and plotted using Python. For VAE, the learning rate is an important parameter in the VAE model; a too-high learning rate will lead to rapid loss, while a too-small learning rate will lead to slow convergence or overfitting of the model. For the setting of the learning rate, this paper tries to use 0.1, 0.01, 0.001, 0.0001, 0.00001, and other test values, and determines that the learning rate is 0.001 after the test. We set \(n\) in the attention mechanism to 3. In this study, we employed NS2 simulation software to model attack scenarios among satellite nodes, and the relevant parameters of the satellite network were shown in Table 3. Specifically, we initially constructed a regular node topology within NS2 using code to simulate communication between normal nodes. To introduce malicious nodes among the normal ones, we integrated relevant Tcl code for malicious attacks into the code of normal nodes. These malicious codes are preinstalled with the NS2 software. When the nodes execute the ant colony algorithm, malicious nodes intentionally discard received data packets, thereby introducing the effects of malicious attacks and disrupting the normal operation of the network. These malicious nodes can initiate different types of attacks. A black hole attack involves discarding data packets with a 100% probability upon receipt. A slander attack provides false trust recommendations while computing indirect trust values. Additionally, a selfish attack deliberately drops data packets at a certain rate to conserve energy. In our paper, we assigned black hole attacks to 5 nodes, slander attacks to another 5 nodes, and selfish attacks to the remaining 6 nodes within a network of 16 nodes. These malicious nodes integrated into the normal network are then utilized to assess the reliability of the intelligent security trust model proposed in this study. To test the situation where the satellite network is in the lower route of the malicious attack environment, malicious nodes need to be added in the process of its routine, and different types of attack behavior will be launched by malicious nodes. NS2 simulation software itself has a malicious attack code, so the number of malicious nodes can be set in the satellite network node environment from 0 to 16. These malicious nodes can launch witch attacks, black holes attacks, defamation attacks, selfish attacks, and other attacks.