# Is Machine Learning in Power Systems Vulnerable?

arXiv: https://arxiv.org/abs/1808.08197 · HTML: https://mlsanity.com/paper/1808.08197/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2018.

## Built on by (most cited first, 12 of 12)

- [Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation](https://mlsanity.com/paper/2301.03766/) (2023)
- [Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids](https://mlsanity.com/paper/2102.09057/) (2021)
- [Physics-Constrained Backdoor Attacks on Power System Fault Localization](https://mlsanity.com/paper/2211.04445/) (2022)
- [Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models](https://mlsanity.com/paper/2311.07110/) (2023)
- [Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges](https://mlsanity.com/paper/2501.16490/) (2025)
- [Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation](https://mlsanity.com/paper/2311.06973/) (2023)
- [Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition](https://mlsanity.com/paper/2402.11664/) (2024)
- [Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment](https://mlsanity.com/paper/2407.08886/) (2024)
- [Adversarial Attacks and Defense Methods for Power Quality Recognition](https://mlsanity.com/paper/2202.07421/) (2022)
- [Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids](https://mlsanity.com/paper/2303.18136/) (2023)
- [Generating adversarial inputs for a graph neural network model of AC power flow](https://mlsanity.com/paper/2602.17975/) (2026)
- [Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?](https://mlsanity.com/paper/2607.06632/) (2026)

## Abstract (arXiv)

Recent advances in Machine Learning(ML) have led to its broad adoption in a series of power system applications, ranging from meter data analytics, renewable/load/price forecasting to grid security assessment. Although these data-driven methods yield state-of-the-art performances in many tasks, the robustness and security of applying such algorithms in modern power grids have not been discussed. In this paper, we attempt to address the issues regarding the security of ML applications in power systems. We first show that most of the current ML algorithms proposed in power systems are vulnerable to adversarial examples, which are maliciously crafted input data. We then adopt and extend a simple yet efficient algorithm for finding subtle perturbations, which could be used for generating adversaries for both categorical(e.g., user load profile classification) and sequential applications(e.g., renewables generation forecasting). Case studies on classification of power quality disturbances and forecast of building loads demonstrate the vulnerabilities of current ML algorithms in power networks under our adversarial designs. These vulnerabilities call for design of robust and secure ML algorithms for real world applications.