Optimal Myopic Attacks on Nonlinear Estimation

Prior works have analyzed the security of estimation and control (E&C) for linear, time-invariant systems; however, there are few analyses of nonlinear systems despite their broad safety-critical use. We define two attack objectives on nonlinear E&C and illustrate that realizing the optimal...

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Bibliographic Details
Main Authors: Hallyburton, R. Spencer, Khazraei, Amir, Pajic, Miroslav
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Estimation
Kalman filters
Monte Carlo methods
Runtime
Security
Upper bound
Online Access:Request full text
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Summary:Prior works have analyzed the security of estimation and control (E&C) for linear, time-invariant systems; however, there are few analyses of nonlinear systems despite their broad safety-critical use. We define two attack objectives on nonlinear E&C and illustrate that realizing the optimal attacks against the widely-adopted extended Kalman filter with industry-standard χ 2 anomaly detection is equivalent to solving convex quadratically-constrained quadratic programs. Although these require access to the true state of the system, we provide practical relaxations on the optimal attacks to allow for execution at runtime given a specified amount of attacker knowledge. We show that the difference between the optimal and relaxed attacks is bounded by the attacker knowledge.
ISSN:2576-2370
DOI:10.1109/CDC51059.2022.9992711