A Bayesian approach to joint attack detection and resilient state estimation

  • Nicola Forti
  • , Giorgio Battistelli
  • , Luigi Chisci
  • , Bruno Sinopoli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

The paper deals with resilient state estimation of cyber-physical systems subject to switching signal attacks and fake measurement injection. In particular, the random set paradigm is adopted in order to model the switching nature of the signal attack and the fake measurement injection via Bernoulli and/or Poisson random sets. The problem of jointly detecting a signal attack and estimating the system state in presence of fake measurements is then formulated and solved in the Bayesian framework leading to the analytical derivation of a hybrid Bernoulli filter that updates in real-time the joint posterior density of the detection attack Bernoulli set and of the state vector. Exploiting a Gaussian-mixture implementation of the filter, a simulation example is developed in order to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1192-1198
Number of pages7
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Conference

Conference55th IEEE Conference on Decision and Control, CDC 2016
Country/TerritoryUnited States
CityLas Vegas
Period12/12/1612/14/16

Keywords

  • Bayesian state estimation
  • Bernoulli filter
  • Cyber-physical systems
  • signal attack

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