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.
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This content will become publicly available on September 26, 2026
Affine Transformation-Based Perfectly Undetectable False Data Injection Attacks From Controller’s Perspective on State- and Output Feedback Linear Control Systems
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.
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- PAR ID:
- 10653718
- Publisher / Repository:
- IEEE Transactions on Industrial Cyber-physical Systems
- Date Published:
- Journal Name:
- IEEE Transactions on Industrial Cyber-Physical Systems
- Volume:
- 3
- ISSN:
- 656 - 664
- Page Range / eLocation ID:
- 656 to 664
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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