# Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids

arXiv: https://arxiv.org/abs/2303.18136 · HTML: https://mlsanity.com/paper/2303.18136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Defensive Distillation is Not Robust to Adversarial Examples](https://mlsanity.com/paper/1607.04311/) (2016)
- [Is Machine Learning in Power Systems Vulnerable?](https://mlsanity.com/paper/1808.08197/) (2018)
- [Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach](https://mlsanity.com/paper/1911.08011/) (2019)
- [Assessing the Impact of a User-Item Collaborative Attack on Class of Users](https://mlsanity.com/paper/1908.07968/) (2019)
- [Towards Effective Device-Aware Federated Learning](https://mlsanity.com/paper/1908.07420/) (2019)
- [Prioritized Multi-Criteria Federated Learning](https://mlsanity.com/paper/2007.08893/) (2020)

## Abstract (arXiv)

In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks