Multi-agent system for detecting false data injection attacks against the power grid

Esther Amullen, Hui Lin, Zbigniew T Kalbarczyk, Lee Keel

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

Abstract

A class of cyber-attacks called False Data Injection attacks that target measurement data used for state estimation in the power grid are currently under study by the research community. These attacks modify sensor readings obtained from meters with the aim of misleading the control center into taking ill-advised response action. It has been shown that an attacker with knowledge of the network topology can craft an attack that bypasses existing bad data detection schemes (largely based on residual generation) employed in the power grid. We propose a multi-agent system for detecting false data injection attacks against state estimation. The multi-agent system is composed of software implemented agents created for each substation. The agents facilitate the exchange of information including measurement data and state variables among substations. We demonstrate that the information exchanged among substations, even untrusted, enables agents cooperatively detect disparities between local state variables at the substation and global state variables computed by the state estimator. We show that a false data injection attack that passes bad data detection for the entire system does not pass bad data detection for each agent.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd Annual Industrial Control System Security Workshop, ICSS 2016
PublisherAssociation for Computing Machinery
Pages38-44
Number of pages7
ISBN (Electronic)9781450347884
DOIs
StatePublished - Dec 6 2016
Event2nd Annual Industrial Control System Security Workshop, ICSS 2016 - Los Angeles, United States
Duration: Dec 6 2016 → …

Publication series

NameACM International Conference Proceeding Series

Other

Other2nd Annual Industrial Control System Security Workshop, ICSS 2016
CountryUnited States
CityLos Angeles
Period12/6/16 → …

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Amullen, E., Lin, H., Kalbarczyk, Z. T., & Keel, L. (2016). Multi-agent system for detecting false data injection attacks against the power grid. In Proceedings - 2nd Annual Industrial Control System Security Workshop, ICSS 2016 (pp. 38-44). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3018981.3018987