Malicious data attacks against dynamic state estimation in the presence of random noise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

State estimation in a discrete-time linear dynamical system is considered in the presence of random process noise, in addition to a malicious adversary able to manipulate some measurements of the system. The adversary has access to only a subset of the measurements, but the particular subset is unknown, and it may adjust these measurements in an arbitrary fashion. A specific attack is proposed that gives a lower bound on the mean squared error for any estimator. Two estimators are proposed; one based on a non-convex optimization problem using sparsity constraints, the second a convex relaxation using a mixed ℓ1/ℓ2 norm. The performance of both estimators are studied using simulations.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages261-264
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Byzantine attack
  • Cyber-security
  • Dynamic state estimation
  • Malicious data attacks

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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