# Detection of False Data Injection Attacks Using the Autoencoder Approach

arXiv: https://arxiv.org/abs/2003.02229 · HTML: https://mlsanity.com/paper/2003.02229/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Generating Multivariate Load States Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2110.11435/) (2021)
- [A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction](https://mlsanity.com/paper/2405.02180/) (2024)
- [Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids](https://mlsanity.com/paper/2102.09057/) (2021)
- [Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks](https://mlsanity.com/paper/2606.08473/) (2026)

## Abstract (arXiv)

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.