Two-fold Intelligent Approach for Successful FDI Attack on Power Systems State Estimation

Abdullah Sawas, Hany E.Z. Farag

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations

    Abstract

    Recent research works have revealed that state estimators in power systems are susceptible to false data injection attacks (FDIA). Still, for an adversary, constructing a least effort attack vector is difficult and known to be L 0 -norm optimization problem. In this paper, two-fold intelligent approach is proposed to optimally construct the FDIA vector. First, the problem of selecting the vector components is formulated as a constrained nonlinear programming problem and is solved using Genetic Algorithm. Second, a Neural Network is trained to generate in real-time the vector amplitudes. The attack vector is optimally selected in terms of number of measurements to compromise, the set of measurements accessible be the adversary, and flexibility to successfully pass Bad Data Detection algorithm of the state estimator. The performance of the attack vectors is analyzed on the IEEE 14-bus system against AC state estimator for a range of various system loading conditions and considering two attack strategies.

    Original languageBritish English
    Title of host publication2018 IEEE Electrical Power and Energy Conference, EPEC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538654194
    DOIs
    StatePublished - 31 Dec 2018
    Event2018 IEEE Electrical Power and Energy Conference, EPEC 2018 - Toronto, Canada
    Duration: 10 Oct 201811 Oct 2018

    Publication series

    Name2018 IEEE Electrical Power and Energy Conference, EPEC 2018

    Conference

    Conference2018 IEEE Electrical Power and Energy Conference, EPEC 2018
    Country/TerritoryCanada
    CityToronto
    Period10/10/1811/10/18

    Keywords

    • AC State Estimation
    • False Data Injection
    • Genetic Algorithm
    • Neural Networks
    • Smart Grid Security

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