Toward Intelligent Access Authentication in Integrated Satellite-Terrestrial Backhaul Networks: Focusing on Spatial-Temporal Characteristics

Due to the characteristics of open links, immense coverage and dynamic network topology, access authentication is crucial and challenging in integrated satellite-terrestrial back-haul networks (ISTBNs). Based on the access behavior of users, this paper proposes an ISTBN access authentication approach to prevent the spoofing attack, which is the most prominent security threat in ISTBNs. Concretely, we model the access authentication as a machine learning classification task with spatial-temporal characteristics. In this way, the dependency on identification information can be avoided, which is easy to be copied by adversaries in spoofing attacks. Then, we design an ensemble learning classifier to judge the legality of the access to ISTBNs. By organically integrating tree-based models and deep learning through the soft voting method, the detection performance of unauthorized access can be improved. Experiment results validate the effectiveness of our approach for preventing unauthorized access to ISTBNs.