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

arXiv: https://arxiv.org/abs/2508.20504 · HTML: https://mlsanity.com/paper/2508.20504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities](https://mlsanity.com/paper/2112.14792/) (2021)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey](https://mlsanity.com/paper/1806.00121/) (2018)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2501.11074/) (2025)
- [Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure](https://mlsanity.com/paper/2001.00916/) (2019)
- [Russian Cyber Onslaught was Blunted by Ukrainian Cyber Resilience, not Merely Security](https://mlsanity.com/paper/2408.14667/) (2024)

## Built on by (most cited first, 3 of 3)

- [Taming Silent Failures: A Framework for Verifiable AI Reliability](https://mlsanity.com/paper/2510.22224/) (2025)
- [GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics](https://mlsanity.com/paper/2510.07109/) (2025)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)

## Abstract (arXiv)

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.