Attack-Resilient State Estimation in the Presence of Noise
Repository Usage Stats
249
views
views
290
downloads
downloads
Abstract
We consider the problem of attack-resilient state estimation in the presence of noise.
We focus on the most general model for sensor attacks where {any} signal can be injected
via the compromised sensors. An $l_0$-based state estimator that can be formulated
as a mixed-integer linear program and its convex relaxation based on the $l_1$ norm
are presented. For both $l_0$ and $l_1$-based state estimators, we derive rigorous
analytic bounds on the state-estimation errors. We show that the worst-case error
is linear with the size of the noise, meaning that the attacker cannot exploit noise
and modeling errors to introduce unbounded state-estimation errors. Finally, we show
how the presented attack-resilient state estimators can be used for sound attack detection
and identification, and provide conditions on the size of attack vectors that will
ensure correct identification of compromised sensors.
Type
ConferencePermalink
https://hdl.handle.net/10161/11286Collections
More Info
Show full item recordScholars@Duke
Miroslav Pajic
Dickinson Family Associate Professor
Miroslav Pajic's research focuses on design and analysis of cyber-physical systems
with varying levels of autonomy and human interaction, at the intersection of (more
traditional) areas of embedded systems, AI, learning and controls, formal methods
and robotics.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info