# Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids arXiv: https://arxiv.org/abs/2104.02012 ยท HTML: https://mlsanity.com/paper/2104.02012/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 62 references that are in the map. - First posted on arXiv 5 Apr 2021. ## Builds on (most cited first, 9 of 9) - [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016) - [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018) - [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017) - [Optimizing the Latent Space of Generative Networks](https://mlsanity.com/paper/1707.05776/) (2017) - [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017) - [Optimal Power Flow Using Graph Neural Networks](https://mlsanity.com/paper/1910.09658/) (2019) - [Detection of false data injection attacks in smart grids based on graph signal processing](https://mlsanity.com/paper/1810.04894/) (2018) - [Monitoring and Optimization for Power Grids: A Signal Processing Perspective](https://mlsanity.com/paper/1302.0885/) (2013) - [Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid](https://mlsanity.com/paper/1803.00128/) (2018) ## Built on by (most cited first, 24 of 24) - [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022) - [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024) - [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021) - [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) - [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024) - [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021) - [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) - [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023) - [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024) - [Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method](https://mlsanity.com/paper/2409.04609/) (2024) - [Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems](https://mlsanity.com/paper/2508.10035/) (2025) - [GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks](https://mlsanity.com/paper/2606.19566/) (2026) - [Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids](https://mlsanity.com/paper/2206.12527/) (2022) - [Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertainty](https://mlsanity.com/paper/2305.16469/) (2023) - [XFlow: Benchmarking Flow Behaviors over Graphs](https://mlsanity.com/paper/2308.03819/) (2023) - [Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs](https://mlsanity.com/paper/2406.02927/) (2024) - [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024) - [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) - [Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection](https://mlsanity.com/paper/2603.00085/) (2026) - [Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization](https://mlsanity.com/paper/2605.22749/) (2026) - [Graph Machine Learning: An Opportunity for Power Systems](https://mlsanity.com/paper/2608.16494/) (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 (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.