# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
###### Abstract  
The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D-S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput. t +
Footnote †: journal: Sensors  
2023238474[[https://doi.org/10.3390/s23208474](https://doi.org/10.3390/s23208474)]([https://doi.org/10.3390/s23208474](https://doi.org/10.3390/s23208474))  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Satellite Network Security Routing Technology Based on Deep Learning and Trust Management. _Sensors_**2023**, 23, 8474. [[https://doi.org/10.3390/s23208474](https://doi.org/10.3390/s23208474)]([https://doi.org/10.3390/s23208474](https://doi.org/10.3390/s23208474))  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Satellite Network Security Routing Technology Based on Deep Learning and Trust Management. _Sensors_**2023**, 23, 8474.  
yielded numerous achievements. Among these, secure routing in satellite networks has received considerable attention. The security of satellite network routing is primarily considered from two aspects: encryption and trust management [6]. Initially, researchers fortified routing data from a cryptographic perspective, which constitutes an external defense. Employing such defensive measures involves constant encryption and decryption of transmitted data by satellite nodes, leading to relatively low efficiency. Moreover, a critical flaw in this approach is its inability to prevent attackers from breaching encryption barriers and impersonating legitimate nodes. Once a satellite node is compromised as a malicious entity, encryption loses its protective effect on the satellite network, posing substantial threats to satellite network routing and jeopardizing the overall system's security [7]. Therefore, in the current research on secure satellite network routing, countering internal attacks where attackers mimic normal nodes is a central concern. Traditional approaches to defending against internal attacks in satellite network routing involve trust management models, which can be simplified trust models using weighting and more complex mathematical trust models. With the emergence of various novel attack methods, the efficacy of traditional trust models in detecting malicious attacks has declined. This is due to their simplistic mathematical models that lack precision in modeling actual node attack behaviors. In the face of evolving attack methods, traditional trust models struggle to extract attack features effectively. Consequently, there is a pressing need to enhance traditional trust models.