False data injection attacks on power system state estimation with limited information

Jiazi Zhang, Zhigang Chu, Lalitha Sankar, Oliver Kosut

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

10 Citations (Scopus)

Abstract

This paper studies physical consequences of worst-case unobservable false data injection (FDI) attacks on the electric power system. In particular the focus is on FDI attacks wherein the attacker can only change measurements in an attack sub-network of the entire network and has limited knowledge of network parameters outside of this sub-network. The goal of this limited resource attack is to cause a physical line overflow for a chosen target line that will not be observable via measurements. To determine the worst possible consequences of such a class of FDI attacks, a bi-level optimization problem is introduced wherein the first level focuses on maximizing the target line flow subject to limited attack resources constraints, while the second level formulates system response to such attacks via DC optimal power flow (OPF). The attack model with limited system knowledge is reflected in the DC OPF formulation that only takes into account the system information for the attack sub-network and therefore is oblivious of congestion and data for the network outside this sub-network. The vulnerability of this attack model is illustrated for the IEEE 24-bus RTS system.

Original languageEnglish (US)
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE Computer Society
Volume2016-November
ISBN (Electronic)9781509041688
DOIs
StatePublished - Nov 10 2016
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
Duration: Jul 17 2016Jul 21 2016

Other

Other2016 IEEE Power and Energy Society General Meeting, PESGM 2016
CountryUnited States
CityBoston
Period7/17/167/21/16

Fingerprint

State estimation
Electric power systems
Information systems

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Zhang, J., Chu, Z., Sankar, L., & Kosut, O. (2016). False data injection attacks on power system state estimation with limited information. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 (Vol. 2016-November). [7741928] IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741928

False data injection attacks on power system state estimation with limited information. / Zhang, Jiazi; Chu, Zhigang; Sankar, Lalitha; Kosut, Oliver.

2016 IEEE Power and Energy Society General Meeting, PESGM 2016. Vol. 2016-November IEEE Computer Society, 2016. 7741928.

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

Zhang, J, Chu, Z, Sankar, L & Kosut, O 2016, False data injection attacks on power system state estimation with limited information. in 2016 IEEE Power and Energy Society General Meeting, PESGM 2016. vol. 2016-November, 7741928, IEEE Computer Society, 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, United States, 7/17/16. https://doi.org/10.1109/PESGM.2016.7741928
Zhang J, Chu Z, Sankar L, Kosut O. False data injection attacks on power system state estimation with limited information. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016. Vol. 2016-November. IEEE Computer Society. 2016. 7741928 https://doi.org/10.1109/PESGM.2016.7741928
Zhang, Jiazi ; Chu, Zhigang ; Sankar, Lalitha ; Kosut, Oliver. / False data injection attacks on power system state estimation with limited information. 2016 IEEE Power and Energy Society General Meeting, PESGM 2016. Vol. 2016-November IEEE Computer Society, 2016.
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