# False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2111.14251 · HTML: https://mlsanity.com/paper/2111.14251/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 172 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [Cyber-Security in Smart Grid: Survey and Challenges](https://mlsanity.com/paper/1809.02609/) (2018)
- [Detection of false data injection attacks in smart grids based on graph signal processing](https://mlsanity.com/paper/1810.04894/) (2018)
- [A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems](https://mlsanity.com/paper/1809.00057/) (2018)
- [Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning](https://mlsanity.com/paper/1808.01094/) (2018)
- [A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems](https://mlsanity.com/paper/2008.06926/) (2020)
- [Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts](https://mlsanity.com/paper/2103.10594/) (2021)
- [Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network](https://mlsanity.com/paper/1809.06976/) (2018)
- [Covert Attacks in Cyber-Physical Control Systems](https://mlsanity.com/paper/1609.09537/) (2016)
- [Impact of integrity attacks on real-time pricing in smart grids](https://mlsanity.com/paper/1602.02860/) (2016)
- [Secure and Privacy-Friendly Local Electricity Trading and Billing in Smart Grid](https://mlsanity.com/paper/1801.08354/) (2018)
- [Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks](https://mlsanity.com/paper/1705.04218/) (2017)
- [Cyber-Physical Simulation Platform for Security Assessment of Transactive Energy Systems](https://mlsanity.com/paper/1903.01520/) (2019)
- [False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics](https://mlsanity.com/paper/1910.01716/) (2019)
- [A Physics-Based Attack Detection Technique in Cyber-Physical Systems: A Model Predictive Control Co-Design Approach](https://mlsanity.com/paper/1910.03162/) (2019)
- [Power Injection Attacks in Smart Distribution Grids with Photovoltaics](https://mlsanity.com/paper/2011.05829/) (2020)

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

- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination](https://mlsanity.com/paper/2303.16956/) (2023)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection](https://mlsanity.com/paper/2603.00085/) (2026)

## Abstract (arXiv)

With the proliferation of smart devices and revolutions in communications, electrical distribution systems are gradually shifting from passive, manually-operated and inflexible ones, to a massively interconnected cyber-physical smart grid to address the energy challenges of the future. However, the integration of several cutting-edge technologies has introduced several security and privacy vulnerabilities due to the large-scale complexity and resource limitations of deployments. Recent research trends have shown that False Data Injection (FDI) attacks are becoming one of the most malicious cyber threats within the entire smart grid paradigm. Therefore, this paper presents a comprehensive survey of the recent advances in FDI attacks within active distribution systems and proposes a taxonomy to classify the FDI threats with respect to smart grid targets. The related studies are contrasted and summarized in terms of the attack methodologies and implications on the electrical power distribution networks. Finally, we identify some research gaps and recommend a number of future research directions to guide and motivate prospective researchers.