# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Analysis of Results
#### 4.2.1 Average End-to-End Delay  
To evaluate the efficacy of the algorithm utilized in this paper, it is designated as TVAE. Subsequent simulations will then compare the performance of TVAE with three other algorithms: ACO (Ant Colony Optimization) [32], TAODV (Trust Ad hoc on-Demand Distance Vector Routing) [20], and SLT (Secure Routing Algorithm of Leo Satellite Network-Based Node Trust) [18]. Average end-to-end delay refers to the time it takes for a packet to be sent from the source node to the destination node. After setting the number and type of malicious nodes in NS2 simulation software, the end-to-end delay data of satellite network routing can be obtained. As shown in the figure below, when there are no malicious nodes in the network, the benchmark algorithm ACO does not introduce a security trust model to harden it, thus saving additional computing time of trust value. Therefore, the average end-to-end delay of the ACO algorithm is the lowest among the four curves. TAODV, SLT, and TVAE all need to carry out security hardening of the trust model. Therefore, when there is no malicious node, the average end-to-end delay has a corresponding time consumption. In the beginning, because the number of malicious nodes is zero, the average end-to-end delay at this time is based on the execution time of the baseline routing algorithm and trust model. As evident from Figure 6, the average end-to-end delay of the ACO algorithm is 9 ms lower than that of the TAODV algorithm, 15 ms lower than the TVAE algorithm, and 18 ms lower than the SLT algorithm. The reasons for the trend of the four curves are as follows: Because the ACO algorithm does not have trust model reinforcement, it simply routes satellite network, so its average end-to-end delay is the smallest, while TAODV,because of the trust model adopted, is a traditional mathematical model which is relatively simple in trust calculation, so its average end-to-end delay is also small. In addition, when the SLT algorithm is running, in addition to running a basic routing module and dynamic trust evaluation processing module, it also uses a dynamic health diagnosis processing module to detect ISL faults, so its time consumption is larger than that of the TVAOD algorithm. The benchmarked algorithm of TVAE, the ant colony algorithm, needs to obtain the safety factors of the VAE module to guide its further routing in the routing process. However, TVAE does not consume the time of the dynamic health diagnosis processing module, so its final average end-to-end delay is slightly smaller than the SLT algorithm. According to the illustration in Figure 6, as the number of malicious nodes increases to three, the curve of the TVAE algorithm crosses the curve of the TAODV algorithm. From this point onwards, when the number of malicious nodes exceeds three, the TAODV algorithm's ability to counter malicious attacks becomes weaker than TVAE's, consequently resulting in its average end-to-end delay surpassing that of TVAE. Additionally, as the number of malicious nodes increases from zero to four, the ACO algorithm maintains a consistently low average end-to-end delay. The reason behind this outcome is that the ACO algorithm lacks a security trust model; therefore, it does not incur time overhead from security mechanisms. Moreover, due to the inherent capability of the ACO algorithm's pheromones to dynamically perceive the quality of paths, it can dynamically choose alternative paths in the face of malicious attacks. Specifically, when a malicious node initiates a packet-dropping event, the ants in the ACO algorithm notice during multiple path exploration instances that this path is not viable, leading to minimal pheromone deposition on that path. Consequently, the ants abandon this path and prefer paths with higher pheromone levels. Ultimately, the other three algorithms need to account for the delay introduced by the security trust model. As a result, the ACO algorithm maintains a lower average end-to-end delay compared to the other three algorithms.