mlsanityData to 7 Oct 2026

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

arXiv 2401.05896 ↗First posted 11 Jan 2024cs.CR cs.LG cs.NIcited by 2

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Abstract (arXiv)

As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become enticing targets for sophisticated cyberattacks. These evolving threats demand robust security measures to maintain the stability and resilience of modern energy systems. While extensive research has been conducted, a comprehensive exploration of proactive cyber defense strategies utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This survey bridges this gap, studying the latest DL techniques for proactive cyber defense. The survey begins with an overview of related works and our distinct contributions, followed by an examination of SG infrastructure. Next, we classify various cyber defense techniques into reactive and proactive categories. A significant focus is placed on DL-enabled proactive defenses, where we provide a comprehensive taxonomy of DL approaches, highlighting their roles and relevance in the proactive security of SG. Subsequently, we analyze the most significant DL-based methods currently in use. Further, we explore Moving Target Defense, a proactive defense strategy, and its interactions with DL methodologies. We then provide an overview of benchmark datasets used in this domain to substantiate the discourse.{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.} The survey finally lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 178 references that are in the map. First posted on arXiv 11 Jan 2024.

Builds on

25 of 178 references.

References · 25

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13 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguardcited by 3
  2. 2026A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Gridscited by 0