Smart Grid networks, which rely on advanced technologies for energy generation, distribution, and consumption, are increasingly susceptible to cyber-attacks. This paper surveys the role of Deep Learning (DL) in enhancing proactive security measures for these networks. The authors provide a taxonomy of DL approaches, discussing their relevance in the proactive security of Smart Grids. They also explore Moving Target Defense, another proactive defense strategy, and its interactions with DL methodologies. The paper highlights the challenges of deploying DL-based security systems within Smart Grids and envisions future developments in this field.
Publication date: 15 Jan 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2401.05896
