Decentralized False-Data Injection Attacks Against State Omniscience: Existence and Security Analysis

• Published in: IEEE transactions on automatic control Vol. 68; no. 8; pp. 4634 - 4649
• Main Authors: Zhang, Tian-Yu, Ye, Dan, Shi, Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Complete stealthiness; cyber-physical system security; decentralized false-data injection (DFDI) attacks; Detectors; distributed observers; Eigenvalues; Eigenvalues and eigenfunctions; Estimation error; Kalman filters; Observers; Security; Sensors; Sequences; state omniscience; Wireless communication
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2022.3209396

This article focuses on how false-data injection (FDI) attacks compromise state omniscience, which needs each node in a jointly detectable sensor network to estimate the entire plant state through distributed observers. To reveal the global vulnerability of state omniscience, we investigate decentralized FDI (DFDI) attacks that destabilize the estimation error dynamics but eliminate their influences on the residual in each sensor node. First, the sufficiency and necessity for the existence of such attacks are studied from system eigenvalues and attackable sensors. Second, the self-generated DFDI attack sequences independent of system real-time data are designed to achieve the attack objective with elaborate parameters. Especially, the DFDI attack sequences are improved to maintain real values even if the system matrix only has unstable imaginary eigenvalues. Finally, we analyze the secure range for observer interaction weights and the sensor protection scheme to guarantee the security of state omniscience under DFDI attacks. The theoretical results for DFDI attacks are demonstrated with the linearized discrete-time model of an aircraft system.