Securing State Estimation Under Sensor and Actuator Attacks: Theory and Design
This addresses security for control systems vulnerable to adversarial attacks, presenting a novel theoretical condition and estimator, though it is incremental in applying existing solving techniques to this specific problem.
The paper tackles the problem of state estimation in linear time-invariant systems under arbitrary sensor and actuator attacks, introducing sparse strong observability as a necessary and sufficient condition for correct state reconstruction and proposing an estimator using satisfiability modulo theory solving, with numerical simulations showing effectiveness and scalability.
This paper discusses the problem of estimating the state of a linear time-invariant system when some of its sensors and actuators are compromised by an adversarial agent. In the model considered in this paper, the malicious agent attacks an input (output) by manipulating its value arbitrarily, i.e., we impose no constraints (statistical or otherwise) on how control commands (sensor measurements) are changed by the adversary. In the first part of this paper, we introduce the notion of sparse strong observability and we show that is a necessary and sufficient condition for correctly reconstructing the state despite the considered attacks. In the second half of this work, we propose an estimator to harness the complexity of this intrinsically combinatorial problem, by leveraging satisfiability modulo theory solving. Numerical simulations demonstrate the effectiveness and scalability of our estimator.