Probabilistic state estimation for labeled continuous time Markov models with applications to attack detection
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 associ...
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| Veröffentlicht in: | Discrete event dynamic systems 2022-03, Vol.32 (1), p.65-88 |
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| container_title | Discrete event dynamic systems |
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| creator | Lefebvre, Dimitri Seatzu, Carla Hadjicostis, Christoforos N. Giua, Alessandro |
| description | 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. |
| doi_str_mv | 10.1007/s10626-021-00348-y |
| format | Article |
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| subjects | Automatic Control Engineering Computer Science Control Convex and Discrete Geometry Cybersecurity Electrical Engineering Machines Manufacturing Markov chains Mathematics Mathematics and Statistics Operations Research/Decision Theory Processes State estimation Systems Theory Topical Collection on Cybersecurity |
| title | Probabilistic state estimation for labeled continuous time Markov models with applications to attack detection |
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