# Attention-Enhanced Graph Filtering for False Data Injection Attack Detection and Localization

arXiv: https://arxiv.org/abs/2601.18981 · HTML: https://mlsanity.com/paper/2601.18981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [A Survey on Oversmoothing in Graph Neural Networks](https://mlsanity.com/paper/2303.10993/) (2023)
- [Graph Neural Networks with convolutional ARMA filters](https://mlsanity.com/paper/1901.01343/) (2019)
- [Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding](https://mlsanity.com/paper/2106.02795/) (2021)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [ResiDual: Transformer with Dual Residual Connections](https://mlsanity.com/paper/2304.14802/) (2023)
- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021)
- [Positional Encoding in Transformer-Based Time Series Models: A Survey](https://mlsanity.com/paper/2502.12370/) (2025)
- [A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings](https://mlsanity.com/paper/2104.09079/) (2021)
- [Data-Driven Power Flow Linearization: A Regression Approach](https://mlsanity.com/paper/1710.10621/) (2017)
- [Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention](https://mlsanity.com/paper/2311.17400/) (2023)
- [An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement](https://mlsanity.com/paper/2401.09686/) (2024)
- [Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing](https://mlsanity.com/paper/2006.06095/) (2020)
- [Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems](https://mlsanity.com/paper/2508.10035/) (2025)

## Abstract (arXiv)

The increasing deployment of Internet-of-Things (IoT)-enabled measurement devices in modern power systems has expanded the cyberattack surface of the grid. As a result, this critical infrastructure is increasingly exposed to cyberattacks, including false data injection attacks (FDIAs) that compromise measurement integrity and threaten reliable system operation. Existing FDIA detection methods primarily exploit spatial correlations and network topology using graph-based learning; however, these approaches often rely on high-dimensional representations and shallow classifiers, limiting their ability to capture local structural dependencies and global contextual relationships. Moreover, naively incorporating Transformer architectures can result in overly deep models that struggle to model localized grid dynamics. This paper proposes a joint FDIA detection and localization framework that integrates auto-regressive moving average (ARMA) graph convolutional filters with an Encoder-Only Transformer architecture. The ARMA-based graph filters provide robust, topology-aware feature extraction and adaptability to abrupt spectral changes, while the Transformer encoder leverages self-attention to capture long-range dependencies among grid elements without sacrificing essential local context. The proposed method is evaluated using real-world load data from the New York Independent System Operator (NYISO) applied to the IEEE 14- and 300-bus systems. Numerical results demonstrate that the proposed model effectively exploits both the state and topology of the power grid, achieving high accuracy in detecting FDIA events and localizing compromised nodes.