# Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

arXiv: https://arxiv.org/abs/2001.02289 · HTML: https://mlsanity.com/paper/2001.02289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Adversarial Attacks and Defense Methods for Power Quality Recognition](https://mlsanity.com/paper/2202.07421/) (2022)

## Abstract (arXiv)

In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML) approach, which can respond to a wide range of attack behaviors without detecting outliers. It strikes a balance between enhancing a system's robustness against cyberattacks and maintaining a reasonable degree of forecasting accuracy when there is no attack. Attack models and configurations for the adversarial training were selected and evaluated to achieve the desired level of performance in a simulation study. The results validate the effectiveness and excellent performance of the proposed method.