# Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids

arXiv: https://arxiv.org/abs/2312.04758 · HTML: https://mlsanity.com/paper/2312.04758/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Physics-Guided Deep Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2002.00097/) (2020)
- [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)
- [Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids](https://mlsanity.com/paper/2110.00667/) (2021)
- [A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2310.06948/) (2023)

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

- [Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids](https://mlsanity.com/paper/2401.07465/) (2024)
- [An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids](https://mlsanity.com/paper/2404.02923/) (2024)
- [Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs](https://mlsanity.com/paper/2406.02927/) (2024)
- [Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection](https://mlsanity.com/paper/2603.00085/) (2026)
- [PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning](https://mlsanity.com/paper/2609.28022/) (2026)

## Abstract (arXiv)

The growing trend toward the modernization of power distribution systems has facilitated the installation of advanced measurement units and promotion of the cyber communication systems. However, these infrastructures are still prone to stealth cyber attacks. The existing data-driven anomaly detection methods suffer from a lack of knowledge about the system's physics, lack of interpretability, and scalability issues hindering their practical applications in real-world scenarios. To address these concerns, physics-informed neural networks (PINNs) were introduced. This paper proposes a multivariate physics-informed convolutional autoencoder (PIConvAE) to detect stealthy cyber-attacks in power distribution grids. The proposed model integrates the physical principles into the loss function of the neural network by applying Kirchhoff's law. Simulations are performed on the modified IEEE 13-bus and 123-bus systems using OpenDSS software to validate the efficacy of the proposed model for stealth attacks. The numerical results prove the superior performance of the proposed PIConvAE in three aspects: a) it provides more accurate results compared to the data-driven ConvAE model, b) it requires less training time to converge c) the model excels in effectively detecting a wide range of attack magnitudes making it powerful in detecting stealth attacks.