Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning
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 protecti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9274426/ |
| _version_ | 1819173635596746752 |
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| author | Kun Li Huachun Zhou Zhe Tu Weilin Wang Hongke Zhang |
| author_facet | Kun Li Huachun Zhou Zhe Tu Weilin Wang Hongke Zhang |
| author_sort | Kun Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T20:26:13Z |
| format | Article |
| id | doaj.art-ec94805ed1d244a8aa845ebb0e2c5ab8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:26:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ec94805ed1d244a8aa845ebb0e2c5ab82022-12-21T18:13:44ZengIEEEIEEE Access2169-35362020-01-01821485221486510.1109/ACCESS.2020.30416419274426Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated LearningKun Li0https://orcid.org/0000-0001-9164-7691Huachun Zhou1Zhe Tu2https://orcid.org/0000-0001-9758-6869Weilin Wang3Hongke Zhang4https://orcid.org/0000-0001-8906-813XSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaThe 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.https://ieeexplore.ieee.org/document/9274426/Satellite-terrestrial integrated networkdistributed NIDSsecurity data setfederated learning |
| spellingShingle | Kun Li Huachun Zhou Zhe Tu Weilin Wang Hongke Zhang Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning IEEE Access Satellite-terrestrial integrated network distributed NIDS security data set federated learning |
| title | Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning |
| title_full | Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning |
| title_fullStr | Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning |
| title_full_unstemmed | Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning |
| title_short | Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning |
| title_sort | distributed network intrusion detection system in satellite terrestrial integrated networks using federated learning |
| topic | Satellite-terrestrial integrated network distributed NIDS security data set federated learning |
| url | https://ieeexplore.ieee.org/document/9274426/ |
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