An intelligent encryption decision method for autonomous domain of multilayer satellite network

Abstract

Multi-layer satellite network (MLSN) is an important part of the Space-Air-Ground Integrated Network (SAGIN), it has obvious advantages in coverage density, business processing, service efficiency and other comprehensive capabilities compared with ordinary single-layer satellite networks. However, due to the fact that many space nodes, highly dynamic topology changes and wireless links scattered in free space, MLSNs are facing the dual challenges of inefficient organization and lack of security. In this case, this paper considers providing secure encryption decision support for the return of data obtained by low earth orbit (LEO) observation satellites relying on the local autonomous region of multi-layer satellite space. By quantifying the security strength of encryption algorithms, the work to be optimized is described as the problem of maximizing the security strength under the constraint of return delay of the data. This paper thus proposes a solution based on deep reinforcement learning (DRL), which relies on reasonably setting the key supporting elements such as state, action and reward closely related to the problem to achieve fast and reliable decision output, and the simulation results show that the proposed method has better performance than many comparison methods.