NSF Foundational Research in Robotics (FRR), Geometric Vulnerabilities in Networked Robotic Systems: Analysis of Affine Transformation-Based False Data Injection Attacks and Their Countermeasures (#2516189), PI: Ueda, $691,375, 09/01/2025-08/31/2028
This research develops a comprehensive theoretical framework using Lie group theory and differential geometry to characterize geometric vulnerabilities in networked robotic systems and establishes novel countermeasures based on state monitoring signature functions. The project investigates affine transformation-based false data injection attacks that exploit coordinate transformations to maintain mathematical consistency in robotic dynamics while altering physical behavior. The research team will derive fundamental mathematical relationships between system symmetries and attack vulnerability, then develop signature functions that create mathematical incompatibilities attackers cannot resolve. The approach will be validated through theoretical analysis and experimental testing on three distinct robotic platforms: bilateral teleoperation systems, mobile robots, and robotic manipulators. The signature function countermeasures exploit the principle that while attackers can maintain consistency in plant dynamics through geometric transformations, they cannot simultaneously preserve consistency in carefully designed nonlinear monitoring functions. This research will establish new mathematical tools for analyzing robotic security, provide practical defense mechanisms for real-world systems, and create fundamental knowledge about the intersection of geometry, dynamics, and cybersecurity in robotic systems.