# Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids

arXiv: https://arxiv.org/abs/2102.09057 · HTML: https://mlsanity.com/paper/2102.09057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 30 of 30)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [On Detecting Adversarial Perturbations](https://mlsanity.com/paper/1702.04267/) (2017)
- [Towards Deep Neural Network Architectures Robust to Adversarial Examples](https://mlsanity.com/paper/1412.5068/) (2014)
- [Adversarial Diversity and Hard Positive Generation](https://mlsanity.com/paper/1605.01775/) (2016)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks](https://mlsanity.com/paper/1904.06606/) (2019)
- [ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems](https://mlsanity.com/paper/2003.05631/) (2020)
- [Is Machine Learning in Power Systems Vulnerable?](https://mlsanity.com/paper/1808.08197/) (2018)
- [Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning](https://mlsanity.com/paper/1808.01094/) (2018)
- [Impacts of Bad Data on the PMU based Line Outage Detection](https://mlsanity.com/paper/1502.04236/) (2015)
- [Detection of False Data Injection Attacks Using the Autoencoder Approach](https://mlsanity.com/paper/2003.02229/) (2020)
- [SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection](https://mlsanity.com/paper/2006.03504/) (2020)
- [Adversarial FDI Attack against AC State Estimation with ANN](https://mlsanity.com/paper/1906.11328/) (2019)

## Built on by (most cited first, 2 of 2)

- [Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays](https://mlsanity.com/paper/2506.19302/) (2025)
- [Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids](https://mlsanity.com/paper/2606.20415/) (2026)

## Abstract (arXiv)

False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. To detect such attacks, recent studies have proposed machine learning (ML) techniques, particularly deep neural networks (DNNs). However, most of these methods fail to account for the risk posed by adversarial measurements, which can compromise the reliability of DNNs in various ML applications. In this paper, we present a DNN-based FDIA detection approach that is resilient to adversarial attacks. We first analyze several adversarial defense mechanisms used in computer vision and show their inherent limitations in FDIA detection. We then propose an adversarial-resilient DNN detection framework for FDIA that incorporates random input padding in both the training and inference phases. Our simulations, based on an IEEE standard power system, demonstrate that this framework significantly reduces the effectiveness of adversarial attacks while having a negligible impact on the DNNs' detection performance.