# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
###### Abstract
Furthermore, integrating machine learning into traditional trust models has been explored in the Internet of Things (IoT) [8]. However, using machine learning to improve models presents challenges such as obtaining datasets with attack labels, as many new attack types are unseen, making labeling difficult. Additionally, machine learning's capability to extract data features is limited, particularly for deep-level attack methods. Deep learning, on the other hand, possesses high flexibility and adopt data feature extraction capabilities, particularly in the domain of anomaly detection. Thus, this paper employs deep learning anomaly detection techniques [9] to reinforce and enhance traditional trust models, forming a novel intelligent security model. In addition, considering the focus on secure routing in satellite networks, the foundational routing algorithm requires enhancement. In this regard, the ant colony algorithm is chosen as the benchmark routing algorithm for satellite networks. This selection is due to the abundance of research applying the ant colony algorithm to satellite network routing, indicating its suitability within this context. Furthermore, as this paper intends to employ an intelligent security model to assess satellite node security, traditional static routing algorithms are unable to promptly integrate with the results of the intelligent security model. The ant colony algorithm, being an intelligent optimization algorithm, can dynamically carry the security assessment factor of the intelligent security model. Consequently, this paper combines the ant colony algorithm with the intelligent security model to jointly counter internal attacks in satellite networks while establishing a secure routing path. For the convenience of later elaboration, the proposed algorithm is named IVAE in this paper. The innovation points of this article include the following three aspects:  
1. Due to issues such as electromagnetic interference, network congestion, and satellite node malfunctions, it is challenging to ascertain whether transmission failures are caused by malicious attacks in satellite networks. Addressing this challenge, this paper proposes the application of the Dempster-Shafer (D-S) evidence theory to handle uncertainties in satellite network communication data. By quantifying interaction data within the satellite network, direct trust vectors and indirect trust vectors are formed. The D-S evidence theory integrates these vectors to create a comprehensive trust vector, which mitigates the misjudgment of satellite network node behavior caused by nonmalicious factors. 2. Traditional mathematical models, when used as security trust models, exhibit low detection accuracy and limited flexibility in identifying unknown attacks. Therefore, this paper introduces the variational autoencoder (VAE) from the field of deep learning to discern malicious node behavior. Furthermore, to enhance the VAE's ability to detect anomalous behavior, an attention mechanism is incorporated into the VAE network. Specifically, after the D-S evidence theory processes communication data, the VAE network receives cleansed communication data as input to its encoder. By adjusting the weights of latent variables provided by the encoder using attention mechanisms, the VAE network can more accurately learn the underlying feature information of the trust vector, thus improving the detection of malicious node behavior and enhancing satellite network routing security. 3. By incorporating the security scores derived from the VAE model into the pheromones of the ant colony algorithm, the satellite network can dynamically guide ants to circumvent malicious nodes during the routing process. Specifically, when ants choose the next hop, they consider their pheromones along with the security assessment factors of neighboring nodes. Ants are inclined to choose paths with higher products of pheromones and security factors.