Maximum Distortion Attacks in Electricity Grids

Inaki Esnaola, Samir M. Perlaza, H. Vincent Poor, Oliver Kosut

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Multiple attacker data-injection attack construction in electricity grids with minimum-mean-square-error state estimation is studied for centralized and decentralized scenarios. A performance analysis of the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is considered. In this setting, optimal centralized attack construction strategies are studied. The decentralized case is examined in a game-theoretic setting. A novel utility function is proposed to model this trade-off and it is shown that the resulting game is a potential game. The existence and cardinality of the corresponding set of Nash equilibria of the game is analyzed. Interestingly, the attackers can exploit the correlation among the state variables to facilitate the attack construction. It is shown that attackers can agree on a data-injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offline. For the particular case of two attackers, numerical results based on IEEE test systems are presented.

Original languageEnglish (US)
Article number7447824
Pages (from-to)2007-2015
Number of pages9
JournalIEEE Transactions on Smart Grid
Volume7
Issue number4
DOIs
StatePublished - Jul 2016

Keywords

  • Data-injection attacks
  • decentralized attacks
  • game theory
  • minimum-mean-squareerror (MMSE) estimation

ASJC Scopus subject areas

  • Computer Science(all)

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