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

2025-08-28

AI Summary

The high interconnectivity of the Internet of Energy (IoE) exacerbates adversarial cyber-attack risks targeting critical infrastructure, where conventional defenses fail against topology- and representation-level model manipulations. Method: This paper proposes a network-layer resilience enhancement method grounded in Graph Structure Learning (GSL), jointly optimizing graph topology and node representations to intrinsically improve model robustness against dynamic adversarial attacks. Unlike generic IoT security solutions, our approach is specifically tailored for IoE, establishing a novel “structure-representation co-defense” paradigm. Contribution/Results: Evaluated on real-world security datasets, the method significantly outperforms mainstream baselines across key metrics—including attack detection rate, model stability, and topology recovery capability. It delivers a verifiable, deployable, and resilient defense framework for IoE systems, advancing the state of adversarial robustness in energy-critical networks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Addressing cyber threats in Internet of Energy infrastructure
• Enhancing resilience against adversarial network manipulation attacks
• Developing safeguards for critical energy system interconnectivity

Innovation

• Graph Structure Learning optimizes topology
• Jointly optimizes node representations
• Resists adversarial network model manipulation