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Article Dans Une Revue Discrete Event Dynamic Systems Année : 2022

Correction to: Probabilistic state estimation for labeled continuous time Markov models with applications to attack detection

Résumé

This paper is about state estimation in a timed probabilistic setting. The main contribution is a general procedure to design an observer for computing the probabilities of the states for labeled continuous time Markov models as functions of time, based on a sequence of observations and their associated time stamps that have been collected thus far. Two notions of state consistency with respect to such a timed observation sequence are introduced and related necessary and sufficient conditions are derived. The method is then applied to the detection of cyber-attacks. The plant and the possible attacks are described in terms of a labeled continuous time Markov model that includes both observable and unobservable events, and where each attack corresponds to a particular subset of states. Consequently, attack detection is reformulated as a state estimation problem.

Dates et versions

hal-04484263 , version 1 (29-02-2024)

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Citer

Dimitri Lefebvre, Carla Seatzu, Christoforos Hadjicostis, Alessandro Giua. Correction to: Probabilistic state estimation for labeled continuous time Markov models with applications to attack detection. Discrete Event Dynamic Systems, 2022, 32 (3), pp.539-544. ⟨10.1007/s10626-022-00364-6⟩. ⟨hal-04484263⟩
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