# An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids

arXiv: https://arxiv.org/abs/2404.02923 · HTML: https://mlsanity.com/paper/2404.02923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks](https://mlsanity.com/paper/2009.07769/) (2020)
- [Impact of Data Normalization on Deep Neural Network for Time Series Forecasting](https://mlsanity.com/paper/1812.05519/) (2018)
- [Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward](https://mlsanity.com/paper/2309.10788/) (2023)
- [Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements](https://mlsanity.com/paper/2007.15237/) (2020)
- [Impacts of Bad Data on the PMU based Line Outage Detection](https://mlsanity.com/paper/1502.04236/) (2015)
- [Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids](https://mlsanity.com/paper/2312.04758/) (2023)
- [Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids](https://mlsanity.com/paper/2401.07465/) (2024)
- [Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method](https://mlsanity.com/paper/1912.05103/) (2019)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [Stealth Data Injection Attacks with Sparsity Constraints](https://mlsanity.com/paper/2201.00065/) (2021)

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

- [Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs](https://mlsanity.com/paper/2406.02927/) (2024)

## Abstract (arXiv)

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other unsupervised learning methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.