On Finite-State Stochastic Modeling and Secure Estimation of Cyber-Physical Systems

The problem of secure state estimation and attack detection in cyber-physical systems is considered in this paper. A stochastic modeling framework is first introduced, based on which the attacked system is modeled as a finite-state hidden Markov model with switching transition probability matrices c...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 62; no. 1; pp. 65 - 80
Main Authors: Dawei Shi, Elliott, Robert J., Tongwen Chen
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cyber-physical system (CPS)
Cyber-physical systems
cyber-security
Cybersecurity
Estimation
hidden Markov model (HMM)
Hidden Markov models
Markov chains
Markov decision processes
Markov processes
Numerical models
Probability theory
Process control
secure estimation
State estimation
Stochastic models
System effectiveness
Transition probabilities
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:The problem of secure state estimation and attack detection in cyber-physical systems is considered in this paper. A stochastic modeling framework is first introduced, based on which the attacked system is modeled as a finite-state hidden Markov model with switching transition probability matrices controlled by a Markov decision process. Based on this framework, a joint state and attack estimation problem is formulated and solved. Utilizing the change of probability measure approach, we show that an unnormalized joint state and attack distribution conditioned on the sensor measurement information evolves in a linear recursive form, based on which the optimal estimates can be further calculated by evaluating the normalized marginal conditional distributions. The estimation results are further applied to secure estimation of stable linear Gaussian systems, and extensions to more general systems are also discussed. The effectiveness of the results are illustrated by numerical examples and comparative simulation.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2541919