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

TL;DR

This survey addresses the gap in proactive cybersecurity for smart grids by focusing on deep learning–driven defenses. It presents a taxonomy of DL approaches (autoencoders, RNNs, CNNs, DRL, Transformers, GNNs) and their SG applications, including network security, access control, IDS, alert prioritization, and physical security, as well as Moving Target Defense interactions. It covers benchmark datasets (KDDCUP99, NSL-KDD, UNSW-NB15, CICIDS, N-BaIoT, SGCC, Irish CER) and highlights prominent DL methods and DL–MTD integrations for proactive defense. The discussion identifies key challenges—model complexity, adversarial robustness, data quality, and SG-specific dataset needs—and outlines future directions like model optimization, robust training, data preprocessing, dataset development, and real-world testing to enable practical deployment.

Abstract

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.

Figures (7)

• Figure 1: Paper organization structure.
• Figure 2: SG Architecture
• Figure 3: IDS and IPS Framework
• Figure 4: Applying MTD to Mitigate Injection Attacks in Smart Grids intro_4.
• Figure 5: Illustration of cyber-physical attacks defense mechanisms in SG.