Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard

This is the author's version that has been accepted for publication in IEEE Network. The final version will be available at IEEE Xplore once published. Personal use of this material is permitted. Abstract --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. Additional financial support was provided by National Taiwan University (NTU) and the NTU Core Consortium Project as part of the Higher Education Sprout Project by the Ministry of Education in Taiwan, under Grants NTU-CC-114L895501 and NTU-G0647.