# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 2 Related Research
This scheme adopts a fuzzy trust management model, which has the advantage that it can make a more accurate judgment on the evaluation of the node trust value, but its disadvantage is that the accuracy of the evaluation results begins to decline as the object scale expands. To solve the routing security of low-Earth-orbit satellites, ref. [18] proposed the secure routing algorithm of the LEO satellite network based on node trust (SLT) algorithm, which is based on the distributed trust evaluation model. It calculates the direct trust, indirect trust, and comprehensive trust values among satellite nodes through the D-S evidence theory, and then combines the comprehensive trust value with the basic routing algorithm Orbit Prediction Shortest Path First Routing for Resilient LEO Satellite Networks (OPSPF) to effectively reduce the influence of malicious nodes. It improves the security of satellite network routing, but its disadvantage is that its satellite network routing algorithm is static and difficult to deal with faults. In [19], a decentralized trust management scheme (DTMS) was designed to filter out malicious nodes in satellite networks. This scheme combines the amount of forwarded evidence and the energy consumption rate of nodes to form direct trust and then establishes a trust framework. The novelty of this scheme is that the energy factor of satellite nodes is considered. However, a drawback of this method is that the calculation process of trustworthiness recommendation is complex, consuming a significant amount of computational and time resources. In the paper [20], the proposed algorithm improves the AODV protocol of ground networks based on the characteristics of satellite networks. Additionally, it combines this protocol with a security trust model to effectively counteract malicious attacks in satellite networks. However, the drawback of this algorithm is that the trust evaluation model it employs uses a Bayesian estimation model. The prior probabilities in the Bayesian estimation model need to be predetermined, which could lead to difficulties in achieving an optimal detection level for the established security trust model. Reference [21] introduced the trust mechanism into the vehicle joint network. Since the vehicle network has the problem of inconsistent trust in different regions, this paper proposed the combination of active detection, blockchain technology, and trust model to improve the effectiveness of the vehicle network system in detecting malicious nodes significantly. The drawback of this algorithm is that it applies to the field of the Internet of Things. The trust model in [22] was used to improve the security of wireless sensor networks. Due to the limitation of computing resources and energy resources of wireless sensors, a trust model scheme based on the Pareto frontier optimal solution was proposed in this paper, so that the security of wireless sensors could be better guaranteed. The drawback of this algorithm is that it involves significant computational and energy resource consumption. In [23], the trust model was applied to underwater acoustic sensors. To identify underwater malicious nodes more accurately, deep reinforcement learning was adopted to detect malicious behaviors in this study. Experimental results showed that, due to the adoption of deep learning, this method could detect malicious attacks at a deep level, which greatly ensures the safety of underwater acoustic sensors. The downside of this algorithm is that utilizing deep reinforcement learning to construct underwater sensors requires a substantial amount of computational power and energy consumption. Reference [24] proposed a secure routing scheme that activated trust. The main innovation of this scheme is to avoid black holes by actively creating multiple detection paths, quickly detecting and obtaining node trust, so as to improve the security of data paths.