Attack-resilient state estimation for noisy dynamical systems
Several recent incidents have clearly illustrated the susceptibility of cyberphysical systems (CPS) to attacks, raising attention to security challenges in these systems. The tight interaction between information technology and the physical world has introduced new vulnerabilities that cannot be addressed with the use of standard cryptographic security techniques. Accordingly, the problem of state estimation in the presence of sensor and actuator attacks has attracted significant attention in the past. Unlike the existing work, in this paper, we consider the problem of attackresilient state estimation in the presence of bounded-size noise. We focus on the most general model for sensor attacks where any signal can be injected via compromised sensors. Specifically, we present an I0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the I1 norm. For both attack-resilient state estimators, we derive rigorous analytic bounds on the state-estimation errors caused by the presence of noise. Our analysis shows that the worst-case error is linear with the size of the noise and, thus, the attacker cannot exploit the noise to introduce unbounded state-estimation errors. Finally, we show how the I0 and I1-based attack-resilient state estimators can be used for sound attack detection and identification; we provide conditions on the size of attack vectors that ensure correct identification of compromised sensors.
Duke Scholars
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Related Subject Headings
- 4901 Applied mathematics
- 4606 Distributed computing and systems software
- 4006 Communications engineering
- 0906 Electrical and Electronic Engineering
- 0805 Distributed Computing
- 0102 Applied Mathematics
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- 4901 Applied mathematics
- 4606 Distributed computing and systems software
- 4006 Communications engineering
- 0906 Electrical and Electronic Engineering
- 0805 Distributed Computing
- 0102 Applied Mathematics