Perfectly Undetectable Reflection and Scaling False Data Injection Attacks Via Affine Transformation on Mobile Robot Trajectory Tracking Control
Jun Ueda, Hyukbin Kwon
AI summary
Problem
Networked mobile robots are vulnerable to sophisticated cyber threats, yet existing stealthy attack models often rely on linear approximations or require unrealistic plant knowledge. This paper investigates how attackers can execute perfectly undetectable false data injection attacks on nonlinear mobile robot dynamics using only simple affine transformations.
Approach
The authors derive attack parameters that apply coordinated scaling and reflection to both control commands and observable states, then introduce a State Monitoring Signature Function (SMSF) to detect these manipulations by tracking invariant properties of continuous system states.
Key results
- Formulation of perfectly undetectable FDIAs via affine transformations on nonlinear mobile robot dynamics
- Proof that attacks succeed regardless of the underlying trajectory tracking controller
- Experimental validation of attack execution on a TurtleBot 3 platform
- Development of a State Monitoring Signature Function (SMSF) to detect scaling and reflection manipulations
Why it matters
Reveals a critical, controller-agnostic vulnerability in networked robotics and provides a practical software-based detection framework for CPS security researchers and roboticists.
Abstract
With the increasing integration of cyber-physical sys- tems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade systemperformancewhileremainingundetected.Thisarticleinves- tigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a nonholonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the par- tially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are vali- dated through experiments using a Turtlebot 3 platform, highlight- ing the urgent need for sophisticated detection mechanisms and resilient control strategies to safeguard CPS against such threats. Furthermore, a novel approach for detection of these attacks called the state monitoring signature function (SMSF) is introduced. An example SMSF, a carefully designed function resilient to FDIA, is shown to be able to detect the presence of an FDIA through signatures based on system states.