# Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach

arXiv: https://arxiv.org/abs/1911.08011 · HTML: https://mlsanity.com/paper/1911.08011/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Targeted Adversarial Attacks on Wind Power Forecasts](https://mlsanity.com/paper/2303.16633/) (2023)
- [Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.12561/) (2021)
- [Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities](https://mlsanity.com/paper/2409.10764/) (2024)
- [Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays](https://mlsanity.com/paper/2506.19302/) (2025)
- [Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids](https://mlsanity.com/paper/2303.18136/) (2023)

## Abstract (arXiv)

With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks aiming to manipulate the data such that residual-based bad data detectors cannot detect them, and the perception of system operators or event classifiers changes about the actual event. This paper investigates the impact of adversarial attacks on convolutional neural network-based event cause analysis frameworks. We have successfully verified the ability of adversaries to maliciously misclassify events through stealthy data manipulations. The vulnerability assessment is studied with respect to the number of compromised measurements. Furthermore, a defense mechanism to robustify the performance of the event cause analysis is proposed. The effectiveness of adversarial attacks on changing the output of the framework is studied using the data generated by real-time digital simulator (RTDS) under different scenarios such as type of attacks and level of access to data.