TY - JOUR
T1 - Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems
AU - Yong, Sze Zheng
AU - Zhu, Minghui
AU - Frazzoli, Emilio
N1 - Funding Information:
This article was supported by the National Science Foundation, Grant No. 1239182. M. Zhu is partially supported by ARO W911NF-13-1-0421 (MURI) and NSF CNS-1505664. Authors’ addresses: S. Z. Yong, 551 E Tyler Mall, ERC 305, School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287, USA; email: szyong@asu.edu; M. Zhu, 229 Electrical Engineering West, School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA; email: muz16@psu.edu; E. Frazzoli, ETH Zürich, Institute for Dynamic Systems and Control, ML K 32.1, Sonneggstrasse 3, 8006 Zürich, Switzerland; email: efrazzoli@ethz.ch. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 ACM 2378-962X/2018/06-ART9 $15.00 https://doi.org/10.1145/3204439
Publisher Copyright:
© 2018 ACM.
PY - 2018/6/9
Y1 - 2018/6/9
N2 - In this article, we consider the problem of attack-resilient state estimation, that is, to reliably estimate the true system states despite two classes of attacks: (i) attacks on the switching mechanisms and (ii) false data injection attacks on actuator and sensor signals, in the presence of stochastic process and measurement noise signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of a multiple-model inference algorithm to tackle these security issues. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable signal attacks) and discuss the topics of attack detection, identification, and mitigation under this framework. Simulation examples of switching and false data injection attacks on a benchmark system and an IEEE 68-bus test system show the efficacy of our approach to recover resilient (i.e., asymptotically unbiased) state estimates as well as to identify and mitigate the attacks.
AB - In this article, we consider the problem of attack-resilient state estimation, that is, to reliably estimate the true system states despite two classes of attacks: (i) attacks on the switching mechanisms and (ii) false data injection attacks on actuator and sensor signals, in the presence of stochastic process and measurement noise signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of a multiple-model inference algorithm to tackle these security issues. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable signal attacks) and discuss the topics of attack detection, identification, and mitigation under this framework. Simulation examples of switching and false data injection attacks on a benchmark system and an IEEE 68-bus test system show the efficacy of our approach to recover resilient (i.e., asymptotically unbiased) state estimates as well as to identify and mitigate the attacks.
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U2 - 10.1145/3204439
DO - 10.1145/3204439
M3 - Article
AN - SCOPUS:85066511951
SN - 2378-962X
VL - 2
JO - ACM Transactions on Cyber-Physical Systems
JF - ACM Transactions on Cyber-Physical Systems
IS - 2
M1 - Y
ER -