Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916997004001280 |
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| author | Yang, Guan-Yan Chen, Jui-Ning Wang, Farn Yeh, Kuo-Hui |
| author_facet | Yang, Guan-Yan Chen, Jui-Ning Wang, Farn Yeh, Kuo-Hui |
| contents | The Internet of Energy (IoE) integrates IoT-driven digital communication with power grids to enable efficient and sustainable energy systems. Still, its interconnectivity exposes critical infrastructure to sophisticated cyber threats, including adversarial attacks designed to bypass traditional safeguards. Unlike general IoT risks, IoE threats have heightened public safety consequences, demanding resilient solutions. From the networking-level safeguard perspective, we propose a Graph Structure Learning (GSL)-based safeguards framework that jointly optimizes graph topology and node representations to resist adversarial network model manipulation inherently. Through a conceptual overview, architectural discussion, and case study on a security dataset, we demonstrate GSL's superior robustness over representative methods, offering practitioners a viable path to secure IoE networks against evolving attacks. This work highlights the potential of GSL to enhance the resilience and reliability of future IoE networks for practitioners managing critical infrastructure. Lastly, we identify key open challenges and propose future research directions in this novel research area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard Yang, Guan-Yan Chen, Jui-Ning Wang, Farn Yeh, Kuo-Hui Cryptography and Security Machine Learning Networking and Internet Architecture The Internet of Energy (IoE) integrates IoT-driven digital communication with power grids to enable efficient and sustainable energy systems. Still, its interconnectivity exposes critical infrastructure to sophisticated cyber threats, including adversarial attacks designed to bypass traditional safeguards. Unlike general IoT risks, IoE threats have heightened public safety consequences, demanding resilient solutions. From the networking-level safeguard perspective, we propose a Graph Structure Learning (GSL)-based safeguards framework that jointly optimizes graph topology and node representations to resist adversarial network model manipulation inherently. Through a conceptual overview, architectural discussion, and case study on a security dataset, we demonstrate GSL's superior robustness over representative methods, offering practitioners a viable path to secure IoE networks against evolving attacks. This work highlights the potential of GSL to enhance the resilience and reliability of future IoE networks for practitioners managing critical infrastructure. Lastly, we identify key open challenges and propose future research directions in this novel research area. |
| title | Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard |
| topic | Cryptography and Security Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.20504 |