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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 article investigates 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 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 an FDIA through signatures based on system states. 

This paper investigates the vulnerability of bilat-eral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup en-ables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly un-detectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan. 

Networkedcontrol systems are vulnerable to manipulation via data injection to observed states and control commands, resulting in undesired state trajectories and system instabilities. Adversarial attacks against such systems can be implemented in the form of undetectable attacks such that an observer never notices deviations from expected behavior. Even when protected by homomorphic encryption, these systems remain vulnerable to stealthy and perfectly undetectable attacks due to the malleability of encrypted data. This research develops a defense architecture against such undetectable attacks through the fusion of two complementary detection protocols working in conjunction with encryption. The mechanism’s strengths and weaknesses are analyzed for affine transformation-based perfectly undetectable attacks and covert attacks. The attacks are implemented against a mobile robot, and defense performance is analyzed, resulting in a robust defense mechanism that outperforms previous undetectable attack detection methods in terms of detection accuracy and reliability across the two representative attack types.