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

arXiv: https://arxiv.org/abs/2401.05896 · HTML: https://mlsanity.com/paper/2401.05896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 178 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives](https://mlsanity.com/paper/1811.05372/) (2018)
- [Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense](https://mlsanity.com/paper/2007.10457/) (2020)
- [Cyber-Security in Smart Grid: Survey and Challenges](https://mlsanity.com/paper/1809.02609/) (2018)
- [MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense](https://mlsanity.com/paper/1705.07213/) (2017)
- [Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense](https://mlsanity.com/paper/1909.08092/) (2019)
- [A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems](https://mlsanity.com/paper/2108.02287/) (2021)
- [Morphence: Moving Target Defense Against Adversarial Examples](https://mlsanity.com/paper/2108.13952/) (2021)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids](https://mlsanity.com/paper/2212.03390/) (2022)
- [Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey](https://mlsanity.com/paper/1806.00121/) (2018)
- [Artificial Neural Network for Cybersecurity: A Comprehensive Review](https://mlsanity.com/paper/2107.01185/) (2021)
- [Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach](https://mlsanity.com/paper/2204.12970/) (2022)
- [Cybersecurity in Power Grids: Challenges and Opportunities](https://mlsanity.com/paper/2105.00013/) (2021)
- [Robust Moving Target Defence Against False Data Injection Attacks in Power Grids](https://mlsanity.com/paper/2111.06346/) (2021)
- [Vulnerability Analysis of Smart Grids to GPS Spoofing](https://mlsanity.com/paper/1804.09310/) (2018)
- [Optimal Timing of Moving Target Defense: A Stackelberg Game Model](https://mlsanity.com/paper/1905.13293/) (2019)
- [Privacy-Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning](https://mlsanity.com/paper/2011.03814/) (2020)

## Built on by (most cited first, 2 of 2)

- [Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguard](https://mlsanity.com/paper/2508.20504/) (2025)
- [A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids](https://mlsanity.com/paper/2606.25200/) (2026)

## 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.