Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security
As cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their gr...
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MDPI AG
2025-06-01
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| author | Nalini Manogaran Yamini Bhavani Shankar Malarvizhi Nandagopal Hui-Kai Su Wen-Kai Kuo Sanmugasundaram Ravichandran Koteeswaran Seerangan |
| author_facet | Nalini Manogaran Yamini Bhavani Shankar Malarvizhi Nandagopal Hui-Kai Su Wen-Kai Kuo Sanmugasundaram Ravichandran Koteeswaran Seerangan |
| author_sort | Nalini Manogaran |
| collection | DOAJ |
| description | As cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their growing complexity and digital networking necessitate robust cybersecurity solutions. Recent research indicates that deep learning can improve CPS security through intelligent threat detection and response. We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. This preserves data privacy through decentralized model training and improves complex security threat detection. We report the results of a rigorous evaluation of CICIoT-2023, Edge-IIoTset-2023, and UNSW-NB datasets containing the CPS ShieldNet Fusion model at the forefront in terms of accuracy and effectiveness against several threats in different CPS environments. Therefore, these results underline the potential of the proposed framework to improve CPS security by providing a robust and scalable solution to current problems and future threats. |
| format | Article |
| id | doaj-art-854fbd10c6a64e5eb406a6474dc279c6 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-854fbd10c6a64e5eb406a6474dc279c62025-08-20T02:21:47ZengMDPI AGSensors1424-82202025-06-012512361710.3390/s25123617Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical SecurityNalini Manogaran0Yamini Bhavani Shankar1Malarvizhi Nandagopal2Hui-Kai Su3Wen-Kai Kuo4Sanmugasundaram Ravichandran5Koteeswaran Seerangan6Department of Computer Science and Business Systems, S.A. Engineering College (Autonomous), Chennai 600077, Tamil Nadu, IndiaDepartment of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology (SRMIST), Kattankulathur 603203, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, Tamil Nadu, IndiaDepartment of Electrical Engineering, Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Huwei, Yunlin County 632, TaiwanDepartment of Electro-Optics Engineering, National Formosa University, Huwei, Yunlin County 632, TaiwanDepartment of Electro-Optics Engineering, National Formosa University, Huwei, Yunlin County 632, TaiwanDepartment of CSE (Artificial Intelligence and Machine Learning), S.A. Engineering College (Autonomous), Chennai 600077, Tamil Nadu, IndiaAs cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their growing complexity and digital networking necessitate robust cybersecurity solutions. Recent research indicates that deep learning can improve CPS security through intelligent threat detection and response. We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. This preserves data privacy through decentralized model training and improves complex security threat detection. We report the results of a rigorous evaluation of CICIoT-2023, Edge-IIoTset-2023, and UNSW-NB datasets containing the CPS ShieldNet Fusion model at the forefront in terms of accuracy and effectiveness against several threats in different CPS environments. Therefore, these results underline the potential of the proposed framework to improve CPS security by providing a robust and scalable solution to current problems and future threats.https://www.mdpi.com/1424-8220/25/12/3617cyber–physical systems (CPSs)securityfederated learningoptimizationartificial intelligence (AI)cyber threats |
| spellingShingle | Nalini Manogaran Yamini Bhavani Shankar Malarvizhi Nandagopal Hui-Kai Su Wen-Kai Kuo Sanmugasundaram Ravichandran Koteeswaran Seerangan Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security Sensors cyber–physical systems (CPSs) security federated learning optimization artificial intelligence (AI) cyber threats |
| title | Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security |
| title_full | Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security |
| title_fullStr | Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security |
| title_full_unstemmed | Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security |
| title_short | Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security |
| title_sort | federated learning and eel levy optimization in cps shieldnet fusion a new paradigm for cyber physical security |
| topic | cyber–physical systems (CPSs) security federated learning optimization artificial intelligence (AI) cyber threats |
| url | https://www.mdpi.com/1424-8220/25/12/3617 |
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