mlsanityData to 14 Oct 2026

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

arXiv 2111.14251 ↗First posted 28 Nov 2021cs.CR cs.AI cs.DCcited by 5

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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.

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

17 of 172 references.

References · 17

  1. 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Gridscited by 24
  2. 2015Machine Learning Methods for Attack Detection in the Smart Gridcited by 18
  3. 2018Cyber-Security in Smart Grid: Survey and Challengescited by 13
  4. 2018Detection of false data injection attacks in smart grids based on graph signal processingcited by 12
  5. 2018A Survey on State Estimation Techniques and Challenges in Smart Distribution Systemscited by 10
  6. 2018Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learningcited by 8
  7. 2020A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systemscited by 8
  8. 2021Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impactscited by 6
  9. 2018Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Networkcited by 4
  10. 2016Covert Attacks in Cyber-Physical Control Systemscited by 3
  11. 2016Impact of integrity attacks on real-time pricing in smart gridscited by 2
  12. 2018Secure and Privacy-Friendly Local Electricity Trading and Billing in Smart Gridcited by 2
5 more
  1. 2017Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attackscited by 1
  2. 2019Cyber-Physical Simulation Platform for Security Assessment of Transactive Energy Systemscited by 1
  3. 2019False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analyticscited by 1
  4. 2019A Physics-Based Attack Detection Technique in Cyber-Physical Systems: A Model Predictive Control Co-Design Approachcited by 1
  5. 2020Power Injection Attacks in Smart Distribution Grids with Photovoltaicscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2022FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providerscited by 14
  2. 2022A Secure Federated Learning Framework for Residential Short Term Load Forecastingcited by 7
  3. 2023FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discriminationcited by 1
  4. 2023FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discriminationcited by 1
  5. 2026Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detectioncited by 0