# Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks

arXiv: https://arxiv.org/abs/2104.11846 · HTML: https://mlsanity.com/paper/2104.11846/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 24 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Graph Neural Networks with convolutional ARMA filters](https://mlsanity.com/paper/1901.01343/) (2019)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Autoregressive Moving Average Graph Filtering](https://mlsanity.com/paper/1602.04436/) (2016)
- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Design of graph filters and filterbanks](https://mlsanity.com/paper/1711.02046/) (2017)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [Monitoring and Optimization for Power Grids: A Signal Processing Perspective](https://mlsanity.com/paper/1302.0885/) (2013)
- [A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems](https://mlsanity.com/paper/2008.06926/) (2020)
- [Distributed Autoregressive Moving Average Graph Filters](https://mlsanity.com/paper/1508.05808/) (2015)
- [Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing](https://mlsanity.com/paper/2006.06095/) (2020)

## Built on by (most cited first, 15 of 15)

- [Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization](https://mlsanity.com/paper/2305.19903/) (2023)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids](https://mlsanity.com/paper/2212.03390/) (2022)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI](https://mlsanity.com/paper/2208.08485/) (2022)
- [Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach](https://mlsanity.com/paper/2401.15321/) (2024)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023)
- [Message-passing selection: Towards interpretable GNNs for graph classification](https://mlsanity.com/paper/2306.02081/) (2023)
- [Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids](https://mlsanity.com/paper/2206.12527/) (2022)
- [Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks](https://mlsanity.com/paper/2310.10666/) (2023)
- [Resilient Temporal GCN for Smart Grid State Estimation Under Topology Inaccuracies](https://mlsanity.com/paper/2410.16008/) (2024)
- [Attention-Enhanced Graph Filtering for False Data Injection Attack Detection and Localization](https://mlsanity.com/paper/2601.18981/) (2026)
- [Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks](https://mlsanity.com/paper/2606.08473/) (2026)
- [Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks](https://mlsanity.com/paper/2609.09564/) (2026)

## Abstract (arXiv)

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.