On model-based detectors for linear time-invariant stochastic systems under sensor attacks

Carlos Murguia, Justin Ruths

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, the authors derive tools for tuning the CUSUM procedure in the fault/attack-free case to fulfil the desired detection performance (in terms of false alarm rate). They use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, they characterise the state degradation that a class of attacks can induce the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, they find the upper bound of state degradation that is possible by an undetected attacker. They quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared), which uses a single measurement at a time. Simulations of a chemical reactor with a heat exchanger are presented to illustrate the performance of their tools.
Original languageEnglish
Pages (from-to)1051 - 1061
Number of pages11
JournalIET Control Theory & Applications
Volume13
Issue number8
DOIs
Publication statusPublished - 21 May 2019
Externally publishedYes

Keywords

  • Kalman filters
  • stochastic systems
  • linear systems
  • stochastic processes
  • control engineering computing
  • vectors
  • control system security
  • dynamic detector
  • static detector
  • model-based detectors
  • sensor attacks
  • vector-valued model-based cumulative sum procedure
  • sensor measurements
  • CUSUM procedure
  • false alarm rate
  • state degradation
  • linear time-invariant stochastic systems
  • fault-attack-free case
  • chi-squared fault-attack detection procedure
  • chemical reactor
  • heat exchanger

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