Perfectly Undetectable Reflection and Scaling False Data Injection Attacks via Affine Transformation on Mobile Robot Trajectory Tracking Control

TL;DR

This paper demonstrates how affine transformations can create perfectly undetectable false data injection attacks on mobile robot trajectory control and proposes a new detection method called SMSF to identify such attacks.

Contribution

It introduces a novel affine transformation-based attack method and a resilient detection approach called SMSF for CPS security.

Findings

Affine transformation attacks can be perfectly undetectable.

The SMSF method effectively detects these attacks.

Experimental validation on Turtlebot 3 confirms the approach's viability.

Abstract

With the increasing integration of cyber-physical systems (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 system performance while remaining undetected. This paper investigates perfectly undetectable false data injection attacks (FDIAs) targeting the trajectory tracking control of a non-holonomic mobile robot. The proposed attack method utilizes affine transformations of intercepted signals, exploiting weaknesses inherent in the partially linear dynamic properties and symmetry of the nonlinear plant. The feasibility and potential impact of these attacks are validated through experiments using a Turtlebot 3 platform, highlighting 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 a FDIA through signatures based on systems states.