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The problem of state estimation in the setting of partially-observed discrete event systems subject to cyber attacks is considered. An operator observes a plant through a natural projection that hides the occurrence of certain events. The objective of the operator is that of estimating the current state of the system. The observation is corrupted by an attacker which can insert and erase some sensor readings with the aim of altering the state estimation of the operator. Furthermore, the attacker wants to remain stealthy, namely the operator should not realize that its observation has been corrupted. An automaton, called attack structure, is defined to describe the set of all possible attacks. In more details, first, an unbounded attack structure is obtained by concurrent composition of two state observers, the attacker observer and the operator observer. Then, the attack structure is refined to obtain a supremal stealthy attack substructure. An attack function may be selected from the supremal stealthy attack substructure and it is said harmful when some malicious goal of the attacker is reached, namely if the set of states consistent with the observation produced by the system and the set of states consistent with the corrupted observation belong to a given relation. The proposed approach can be dually used to verify if there exists a harmful attack for the given system: this allows one to establish if the system is safe under attack.
Opacity, as an important property in information-flow security, characterizes the ability of a system to keep some secret information from an intruder. In discrete-event systems, based on a standard setting in which an intruder has the complete knowledge of the systems structure, the standa
This paper deals with the state estimation problem in discrete-event systems modeled with nondeterministic finite automata, partially observed via a sensor measuring unit whose measurements (reported observations) may be vitiated by a malicious attac
This paper focuses on the problem of cyber attacks for discrete event systems under supervisory control. In more detail, the goal of the supervisor, who has a partial observation of the system evolution, is that of preventing the system from reaching
In this paper, an attack-resilient estimation algorithm is presented for linear discrete-time stochastic systems with state and input constraints. It is shown that the state estimation errors of the proposed estimation algorithm are practically exponentially stable.
Recently we developed supervisor localization, a top-down approach to distributed control of discrete-event systems. Its essence is the allocation of monolithic (global) control action among the local control strategies of individual agents. In this