Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks
This paper investigates the enhancement of physical layer security (PHY security) in Reconfigurable Intelligent Surfaces (RIS)-aided terrestrial and non-terrestrial networks (TN/NTN), focusing on the challenges posed by node mobility. In the context of next-generation mobile networks, ensuring secur...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829561/ |
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| author | Yosefine Triwidyastuti Tri Nhu Do Ridho Hendra Yoga Perdana Kyusung Shim Beongku An |
| author_facet | Yosefine Triwidyastuti Tri Nhu Do Ridho Hendra Yoga Perdana Kyusung Shim Beongku An |
| author_sort | Yosefine Triwidyastuti |
| collection | DOAJ |
| description | This paper investigates the enhancement of physical layer security (PHY security) in Reconfigurable Intelligent Surfaces (RIS)-aided terrestrial and non-terrestrial networks (TN/NTN), focusing on the challenges posed by node mobility. In the context of next-generation mobile networks, ensuring secure communication is critical, especially under varying channel conditions caused by mobility. We explore different mobility models, including random walk, Gauss-Markov, and reference point group mobility, to assess their impact on key security metrics such as secrecy capacity and average secrecy rate. To address these challenges, we develop robust algorithms for optimizing the phase-shift configurations of RIS. Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. We also leverage transfer learning to enhance model robustness across different mobility scenarios through domain adaptation. Our results demonstrate the effectiveness of our proposed methods in maintaining high levels of PHY security despite the dynamic nature of the channel conditions and the mobility of nodes. The proposed phase-shift configuration algorithms and ML-based solutions ensure secure and resilient communication in aerial RIS-aided TN/NTN, contributing to the advancement of secure mobile networks. |
| format | Article |
| id | doaj-art-db83ae74dd1e4597bd74cb22d92693fb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db83ae74dd1e4597bd74cb22d92693fb2025-01-10T00:01:06ZengIEEEIEEE Access2169-35362025-01-01135471549010.1109/ACCESS.2025.352617810829561Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile NetworksYosefine Triwidyastuti0https://orcid.org/0000-0003-0574-2540Tri Nhu Do1https://orcid.org/0000-0002-9857-6723Ridho Hendra Yoga Perdana2https://orcid.org/0000-0002-1680-1190Kyusung Shim3https://orcid.org/0000-0003-4851-0811Beongku An4https://orcid.org/0000-0002-0587-3754Department of Software and Communications Engineering, Graduate School, Hongik University, Sejong, Republic of KoreaDepartment of Electrical Engineering, Polytechnique Montréal, Montreal, QC, CanadaDepartment of Software and Communications Engineering, Graduate School, Hongik University, Sejong, Republic of KoreaSchool of Computer Engineering and Applied Mathematics, Hankyong National University, Anseong, Republic of KoreaDepartment of Software and Communications Engineering, Hongik University, Sejong, Republic of KoreaThis paper investigates the enhancement of physical layer security (PHY security) in Reconfigurable Intelligent Surfaces (RIS)-aided terrestrial and non-terrestrial networks (TN/NTN), focusing on the challenges posed by node mobility. In the context of next-generation mobile networks, ensuring secure communication is critical, especially under varying channel conditions caused by mobility. We explore different mobility models, including random walk, Gauss-Markov, and reference point group mobility, to assess their impact on key security metrics such as secrecy capacity and average secrecy rate. To address these challenges, we develop robust algorithms for optimizing the phase-shift configurations of RIS. Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. We also leverage transfer learning to enhance model robustness across different mobility scenarios through domain adaptation. Our results demonstrate the effectiveness of our proposed methods in maintaining high levels of PHY security despite the dynamic nature of the channel conditions and the mobility of nodes. The proposed phase-shift configuration algorithms and ML-based solutions ensure secure and resilient communication in aerial RIS-aided TN/NTN, contributing to the advancement of secure mobile networks.https://ieeexplore.ieee.org/document/10829561/Physical layer securityreconfigurable intelligent surfacereference point group mobilitytransfer learningunmanned aerial vehicle |
| spellingShingle | Yosefine Triwidyastuti Tri Nhu Do Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks IEEE Access Physical layer security reconfigurable intelligent surface reference point group mobility transfer learning unmanned aerial vehicle |
| title | Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks |
| title_full | Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks |
| title_fullStr | Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks |
| title_full_unstemmed | Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks |
| title_short | Transfer Learning-Empowered Physical Layer Security in Aerial Reconfigurable Intelligent Surfaces-Based Mobile Networks |
| title_sort | transfer learning empowered physical layer security in aerial reconfigurable intelligent surfaces based mobile networks |
| topic | Physical layer security reconfigurable intelligent surface reference point group mobility transfer learning unmanned aerial vehicle |
| url | https://ieeexplore.ieee.org/document/10829561/ |
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