False Data Injection Attacks

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Overview

False data injection attacks alter measurements or reported information so that systems make incorrect estimates or decisions, sometimes while evading conventional anomaly checks. Detection methods use physical constraints, residual tests, network cycle structure, subspace learning, and machine-learning models, often evaluated on IEEE power-grid testbeds. Defenses include robust state estimation, sensor isolation, input transformations, active control, and strategic attacker-defender models for settings such as robotics, transportation, and distributed tracking. A central challenge is distinguishing stealthy attacks from legitimate noise or model error, especially with partial observability, inaccurate physical models, or compromised sensors.

Latest papers

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16 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. Resilient Multi-Agent Target Localization and Tracking via BAG-Aware Mutual Information Maximization under False Data Injection Attacks

    Oct 2, 2026Bibek Adhikari, Samrat Chaulagain, Kamesh SubbaraoMulti-Agent System SecurityFalse Data Injection Attacks

    Department of Mechanical and Aerospace Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA

  2. Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees

    Sep 15, 2026Oscar Mogollon Gutierrez, Fatemeh Ghasemi, Mohammadhossein Homaei +2False Data Injection AttacksPhysics-Informed ML

  3. From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks

    Sep 9, 2026Xin Li, Chenhan Xiao, Jonathan Cohen +3False Data Injection AttacksPower Systems

    Faculty of Computer and Information Science, Ben-Gurion-University, Be’er Sheva, Israel · School of Electrical, Computer and Energy Engineering, Arizona State University, AZ, USA · Department of Computer Science, Shamoon College of Engineering, Be’er Sheva, Israel

  4. Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

    Sep 2, 2026Mehdi Zafari, Iman Mohammadi, A. Lee SwindlehurstGraph Neural NetworksIntegrated Sensing and Communication

    University of California Irvine · Irvine, CA, USA

  5. Adversarial Training of Linear Models under Stealthy Attacks

    Aug 26, 2026Lovisa Eriksson, Dave Zachariah, André M. H. TeixeiraAdversarial AttacksAdversarial Robustness

    Department of Information Technology, Uppsala University, Uppsala, Sweden

  6. Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks

    Aug 2, 2026Amir Ahmad Ghods, Mohammadreza DoostmohammadianFalse Data Injection AttacksFault Detection

    Faculty of Mechanical Engineering, Semnan University, Iran

  7. Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

    Jun 18, 2026Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang +2Cyber-Physical SystemsFalse Data Injection Attacks

    Department of Electrical Engineering and Computer Science, University of Tennessee · Department of Computer Science, The University of Illinois at Springfield · Department of Electrical and Computer Engineering, Clemson University, South Carolina

  8. Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks

    Jun 7, 2026Xin Li, Chenhan Xiao, Jonathan Cohen +3False Data Injection AttacksCybersecurity

    Faculty of Computer and Information Science, Ben-Gurion-University, Be’er Sheva, Israel · School of Electrical, Computer and Energy Engineering, Arizona State University, AZ, USA · Department of Computer Science, Shamoon College of Engineering, Be’er Sheva, Israel

  9. Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems

    May 27, 2026Xin Li, Chenhan Xiao, Jonathan Cohen +3False Data Injection AttacksCybersecurity

    Faculty of Computer and Information Science, Ben-Gurion-University, Be’er Sheva, Israel · School of Electrical, Computer and Energy Engineering, Arizona State University, AZ, USA · Department of Computer Science, Shamoon College of Engineering, Be’er Sheva, Israel

  10. Secure Coordination for Vertiport Sequencing in Advanced Air Mobility

    May 20, 2026Jaehan Im, Filippos Fotiadis, Ufuk Topcu +1Multi-Agent CoordinationFalse Data Injection Attacks

    Graduate Research Assistant, Department of Aerospace Engineering and Engineering Mechanics · Postdoctoral Researcher, Oden Institute for Computational Engineering & Sciences · Professor, Oden Institute for Computational Engineering & Sciences +1

  11. Active Defense Against False Data Injection Attacks in Robotic Manipulators

    May 18, 2026Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. PapadopoulosRobotic ControlFalse Data Injection Attacks

    Mälardalen University, Väster˚as, Sweden

  12. GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks

    May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical SystemsPhysics-Informed Generative Modeling

    School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK

  13. Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks

    Apr 13, 2026Axel Andersson, György DánCPS SecurityCyber-Physical Systems

    Department of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden

  14. Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

    Apr 3, 2026Solon Falas, Markos Asprou, Charalambos Konstantinou +1False Data Injection AttacksAdversarial Robustness

    KIOS Center of Excellence, University of Cyprus · CEMSE, King Abdullah University of Science and Technology (KAUST) · Department of Electrical and Computer Engineering, University of Cyprus

  15. From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks

    Mar 13, 2026Gabriele Gualandi, Alessandro V. PapadopoulosCyber-Physical SystemsFalse Data Injection Attacks

    Department of Computer Science and Engineering, Mälardalen University, Västerås, Sweden

  16. Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

    Mar 12, 2026Taha Eghtesad, Yevgeniy Vorobeychik, Aron LaszkaFalse Data Injection AttacksVehicle Routing

    Informatics and Intelligent Systems Pennsylvania State University University Park, USA · Computer Science & Engineering Washington University in St. Louis St. Louis, USA