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.

翻译：随着信息物理系统（CPS）在关键应用中的日益集成，确保其抵御网络攻击的韧性至关重要。一个特别值得关注的威胁是CPS易受欺骗性攻击的脆弱性，这些攻击会降低系统性能，同时保持不被察觉。本文研究了针对非完整移动机器人轨迹跟踪控制的完美隐蔽虚假数据注入攻击（FDIAs）。所提出的攻击方法利用截获信号的仿射变换，利用了非线性被控对象部分线性动态特性与对称性中固有的弱点。通过使用Turtlebot 3平台进行的实验验证了这些攻击的可行性和潜在影响，凸显了开发复杂检测机制和韧性控制策略以保护CPS免受此类威胁的迫切需求。此外，本文还介绍了一种检测此类攻击的新方法，称为状态监测签名函数（SMSF）。一个示例SMSF——一个精心设计的、对FDIA具有韧性的函数——被证明能够通过基于系统状态的签名来检测FDIA的存在。