# Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges

arXiv: https://arxiv.org/abs/2501.16490 · HTML: https://mlsanity.com/paper/2501.16490/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 64 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Torchattacks: A PyTorch Repository for Adversarial Attacks](https://mlsanity.com/paper/2010.01950/) (2020)
- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [Is Machine Learning in Power Systems Vulnerable?](https://mlsanity.com/paper/1808.08197/) (2018)
- [Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability](https://mlsanity.com/paper/2306.15363/) (2023)
- [SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection](https://mlsanity.com/paper/2006.03504/) (2020)
- [Cybersecurity in Power Grids: Challenges and Opportunities](https://mlsanity.com/paper/2105.00013/) (2021)
- [Investigating Man-in-the-Middle-based False Data Injection in a Smart Grid Laboratory Environment](https://mlsanity.com/paper/2110.09162/) (2021)
- [FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids](https://mlsanity.com/paper/2403.17494/) (2024)
- [GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction](https://mlsanity.com/paper/2405.12076/) (2024)

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

- [ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids](https://mlsanity.com/paper/2511.05420/) (2025)
- [Cross-Domain Adversarial Augmentation: Stabilizing GANs for Medical and Handwriting Data Scarcity](https://mlsanity.com/paper/2605.01815/) (2026)

## Abstract (arXiv)

Smart grids are crucial for meeting rising energy demands driven by global population growth and urbanization. By integrating renewable energy sources, they enhance efficiency, reliability, and sustainability. However, ensuring their availability and security requires advanced operational control and safety measures. Although artificial intelligence and machine learning can help assess grid stability, challenges such as data scarcity and cybersecurity threats, particularly adversarial attacks, remain. Data scarcity is a major issue, as obtaining real-world instances of grid instability requires significant expertise, resources, and time. Yet, these instances are critical for testing new research advancements and security mitigations. This paper introduces a novel framework for detecting instability in smart grids using only stable data. It employs a Generative Adversarial Network (GAN) where the generator is designed not to produce near-realistic data but instead to generate Out-Of-Distribution (OOD) samples with respect to the stable class. These OOD samples represent unstable behavior, anomalies, or disturbances that deviate from the stable data distribution. By training exclusively on stable data and exposing the discriminator to OOD samples, our framework learns a robust decision boundary to distinguish stable conditions from any unstable behavior, without requiring unstable data during training. Furthermore, we incorporate an adversarial training layer to enhance resilience against attacks. Evaluated on a real-world dataset, our solution achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks. Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.