The existing satellite-terrestrial integrated networks (STINs) suffer from security and privacy concerns due to the limited resources, poor attack resistance and high privacy requirements of satellite networks. Network Intrusion Detection System (NIDS) is intended to provide a high level of protection for modern network environments, but how to implement distributed NIDS on STINs has not been widely discussed. At the same time, satellite networks have always lacked real and effective security data sets as references. To solve these problems, we propose a distributed NIDS using Federal Learning (FL) in STIN to properly allocate resources in each domain to analyze and block malicious traffic, especially distributed denial-of-service (DDoS) attacks. Specifically, we first design a typical STIN topology, on the basis of which we collect and design security data sets adapted to satellite and terrestrial networks in STIN, respectively. To address the problem of poor attack resistance of satellite networks, we propose a satellite network topology optimization algorithm to reduce the difficulty in tracing malicious packets due to frequent link switching. In order to solve the problem of limited resources and high privacy requirements of satellite networks, we propose an algorithm for FL adaptation to STIN, and build a distributed NIDS using FL in STIN. Finally, we deploy the designed distributed NIDS in a prototype system and evaluate our proposed distributed NIDS with a large number of simulations of randomly generated malicious traffic. Related results demonstrate that the performance of our approach is better than traditional deep learning and intrusion detection methods in terms of malicious traffic recognition rate, packet loss rate, and CPU utilization.

As the continuous in-depth research of sixth generation (6G) technology, the satellite networks in the Space-Air-Ground Integrated Network (SAGIN) have received more and more attention. However, since satellite nodes have the characteristics of limited resources and dynamic link switching, it is important to effectively save energy in satellite networks. In this paper, we focus on how to make Software-Defined Satellite Networks (SDSN) more energy efficient. First, we propose an energy consumption model for satellite networks. Based on this model, we put forward an improved network topology generation algorithm, which can comprehensively consider the link switching energy consumption and the inter-satellite link energy consumption. Then, considering the huge energy consumption caused by abnormal traffic in the satellite network, we propose a DDoS mitigation mechanism in the satellite network, aiming to reduce the extra energy consumption generated by processing abnormal traffic in the satellite node. Finally, through performance evaluation, the proposed network topology generation algorithm and DDoS attack mitigation strategy can effectively reduce network energy consumption.

Aiming at the problems of single detection target of existing distributed denial of service (DDoS) attacks, incomplete detection datasets and privacy caused by shared datasets, we propose a trusted multi-domain DDoS detection method based on federated learning. Firstly, we divide the types of DDoS attacks into different sub-attacks, design the federated learning dataset for DDoS detection in each domain, and use them to realize a more comprehensive detection method of DDoS attacks on the premise of protecting the data privacy of each domain. Secondly, in order to improve the robustness of federated learning and alleviate poisoning attack, we propose a reputation evaluation method based on blockchain, which estimates interaction reputation, data reputation and resource reputation of each participant comprehensively, so as to obtain the trusted federated learning participants and identify the malicious participants. In addition, we also propose a combination scheme of multi-domain detection and distributed knowledge base and design a feature graph of malicious behavior based on a knowledge graph to realize the memory of multi-domain feature knowledge. The experimental results show that the accuracy of most categories of the multi-domain DDoS detection method can reach more than 95% with the protection of datasets, and the reputation evaluation method proposed in this paper has a higher ability to identify malicious participants against the data poisoning attack when the threshold is set to 0.6.

It is well known that the Sixth Generation (6G) communication system integrating multiple access networks promotes the internet of everything world-widely. However, due to the differentiated underlying network protocols, it is difficult to find a general authentication solution to support various authentication methods in different access networks. Blockchain is a new technology that supports network heterogeneity, which provides a potential solution for differentiated authentication. In this paper, we propose a blockchain-based differentiated authentication mechanism for 6G Heterogeneous Networks (HetNets), which can efficiently authenticate user identities through scheduling different authentication methods. Particularly, we analyze the authentication architecture of 6G HetNets and put forward a blockchain-based differentiated authentication framework. Besides, to improve the scalability of user authentication, it is the first time to use various blockchain authentication contracts to represent different authentication methods. Meanwhile, a differentiated authentication management contract is proposed to uniformly manage different authentication contracts to realize differentiated identity authentication. Based on the evaluation of the prototype system, the proposed mechanism can dynamically provide differentiated authentication services (e.g. EAP-MD5, 5G-AKA) with low additional time (milliseconds levels) cost.