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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 demonstrates the fundamental vulnerability of networked linear control systems to perfectly undetectable false data injection attacks (FDIAs) based on affine transformations. The work formulates a generalized FDIA framework that coordinates multiplicative and additive data injections targeting both control commands and observables in networked systems. The paper derives mathematical conditions for executing affine transformation based perfectly undetectable attacks (ATPAs) on state-feedback and output-feedback control systems, with attack capabilities varying based on the attacker’s knowledge of plant dynamics and control gains. The paper examines several attack scenarios, including scaling and general affine transformations, and characterizes the range of system knowledge—from minimum to full—required for different attack types. The paper classifies ATPA into four types based on the feedback structure (state or output) and knowledge requirements: those that match plant dynamics without controller knowledge and those that match closed-loop dynamics by exploiting controller information. The paper examines several attack scenarios and shows how carefully ATPAs can create the illusion of normal system operation while the actual system behavior deviates significantly from intended trajectories. 

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. 

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. 

Cloud-based control is prevalent in many modern control applications. Such applications require security for the sake of data secrecy and system safety. The presented research proposes an encrypted adaptive control framework that can be secured for cloud computing with encryption and without issues caused by encryption overflow and large execution delays. This objective is accomplished by implementing a somewhat homomorphic encryption (SHE) scheme on a modified model reference adaptive controller with accompanying encryption parameter tuning rules. Additionally, this paper proposes a virtual false data injection attack (FDIA) trap based on the SHE scheme. The trap guarantees a probability of attack detection by the adjustment of encryption parameters, thus protecting the system from malicious third parties. The formulated algorithm is then simulated, verifying that after tuning encryption parameters, the encrypted controller produces desired plant outputs while guaranteeing detection or compensation of FDIAs. With the utilization of this novel control framework, adaptively controlled systems will maintain data confidentiality and integrity against malicious adversaries. 

Homomorphic Encryption is a relatively new cryptographic method which, unlike tra- ditional encryption, allows computations to be preformed on encrypted data. Robotic con- trollers can take advantage of these new techniques to increase system security by en- crypting the entire motion control scheme including: sensor signals, model parameters, feedback gains, and perform computation in the ciphertext space to generate motion com- mands without a security hole. However, numerous challenges exist which have limited the wide spread adoption of homomorphically encrypted control systems. The following thesis address several of these pressing issues–cryptographic overflow and heterogenous deployment. Cryptographic overflow is a phenomenon intrinsic to homomorphic ciphers. As en- crypted data is computed on the level of ‘noise’ inside the ciphertext increases, until it becomes too great making decryption impossible, this is known as ‘overflow’. The pri- mary contributor to noise growth is multiplication. Thus, this thesis explores topological sorting methods to find semantically equivalent but syntactically simpler control expres- sions. This allows an encrypted control scheme to preform the same calculation but with fewer multiplications, thus reducing the total amount of noise injected into the system. Furthermore, encrypted calculations impose a hefty computational burden as compared to its unencrypted counterparts. As such, heterogeneous mix of different computing tech- nologies (i.e. CPU, GPU, FPGA) are needed to achieve real-time signal processing. As such, this thesis explores which aspects of an encrypted control system is best suited for which computing technology and describes a deployment strategy to take advantage of these differences.