Hidden Moving Target Defense against False Data Injection in Distribution Network Reconfiguration

Fei Ding, Bo Liu, Hongyu Wu, Anil Pahwa, Ting Liu, Erfan Ibrahim

Research output: Contribution to conferencePaperpeer-review

18 Scopus Citations

Abstract

This paper introduces Moving Target Defense (MTD) in distribution system against False Data Injection (FDI) attacks on the supervisory control and data acquisition (SCADA) system. Based on the AC power flow model, a hidden MTD (HMTD) strategy is constructed in combination with network reconfiguration by minimizing the system loss and line power flow differences before and after the HMTD. The proposed HMTD-based network reconfiguration is formulated as a mixed-integer nonlinear programming (MINLP) problem. A refined Genetic Algorithm (GA) is proposed to solve it. Numerical test is conducted in a modified IEEE-66 bus system. The simulation results show that the proposed model reduces the power loss introduced by the HMTD as well as yields a stealthy MTD to the attackers. The impact of HMTD on the system performance is also compared with that of the existing MTD strategies.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 21 Dec 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: 5 Aug 201810 Aug 2018

Conference

Conference2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/TerritoryUnited States
CityPortland
Period5/08/1810/08/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/CP-5D00-73331

Keywords

  • false data injection
  • genetic algorithm
  • hidden moving target defense
  • network reconfiguration
  • SCADA

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