False Data Injection Attacks

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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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 Attacks Physics-Informed ML
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 Attacks Power 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
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 Networks Integrated Sensing and Communication
University of California Irvine · Irvine, CA, USA
Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks
Aug 2, 2026Amir Ahmad Ghods, Mohammadreza DoostmohammadianFalse Data Injection Attacks Fault Detection
Faculty of Mechanical Engineering, Semnan University, Iran
Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids
Jun 18, 2026Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang +2Cyber-Physical Systems False 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
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 Attacks Cybersecurity
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
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 Attacks Cybersecurity
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
Secure Coordination for Vertiport Sequencing in Advanced Air Mobility
May 20, 2026Jaehan Im, Filippos Fotiadis, Ufuk Topcu +1Multi-Agent Coordination False 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
Active Defense Against False Data Injection Attacks in Robotic Manipulators
May 18, 2026Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. PapadopoulosRobotic Control False Data Injection Attacks
Mälardalen University, Väster˚as, Sweden
GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
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 Attacks Adversarial 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

Sep 15, 2026, cs.AIJ/K move · Enter open · S save

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

Oscar Mogollon Gutierrez, Fatemeh Ghasemi, Mohammadhossein Homaei, Andres Caro+1

False Data Injection Attacks Physics-Informed ML Digital Twins

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City pedestrian counting systems now feed economic indicators, planning decisions and safety operations, yet the twins built on top of them treat the incoming stream as ground truth. We study what happens when it is not. We formalise stealthy false data injection for city-scale pedestrian sensing, where the map from latent flow to observation is far more rank deficient than in the power and water networks for which stealth has been characterised. Our twin estimates directed flows on the pedestrian street graph, assimilates counts through a learned graph-localised gain, and is trained against a flow conservation residual that couples metered and unmetered segments. Detection combines the innovation with that residual, and the alarm threshold is set by adaptive conformal calibration rather than by hand. To measure what the physics buys, we define the attack margin, the relative reduction in worst-case corruption of the estimated flow field, achieved against a white-box adversary that optimises directly through the twin. On six years of Melbourne data the margin reaches 0.54 against a single compromised device and falls to 0.19 when a third of the fleet is compromised, on a network where only 1.18 per cent of walkable segments are metered. Replacing the street graph by a distance graph collapses it to 0.09, which shows that the gain comes from the conservation law rather than from locality.

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