Learning-Based Defense of False Data Injection Attacks in Power System State Estimation

Arnav Kundu, Abhijeet Sahu, Katherine Davis, Erchin Serpedin

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

Abstract

The electric power grid has evolved immensely with time and the modern power grid is dependent on communication networks for efficient transmission and distribution. Since communication networks are vulnerable to various kinds of cyber attacks it is important to detect them and prevent an important machinery like the power grid to get affected from cyber attacks. False data injection attacks (FDIA) are one of the most common attack strategies where an attacker tries to trick the underlying control system of the grid, by injecting false data in sensor measurements to cause disruptions. Our work has focused towards Least Effort attacks of two types i.e., Random and Target Attacks. Further, we propose a data augmented deep learning based solution to detect such attacks in real time. We aim at generating realistic attack simulations on standard IEEE 14 architectures and train neural networks to detect such attacks.

Original languageEnglish
Title of host publication51st North American Power Symposium, NAPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728104072
DOIs
Publication statusPublished - Oct 2019
Event51st North American Power Symposium, NAPS 2019 - Wichita, United States
Duration: 13 Oct 201915 Oct 2019

Publication series

Name51st North American Power Symposium, NAPS 2019

Conference

Conference51st North American Power Symposium, NAPS 2019
CountryUnited States
CityWichita
Period13/10/1915/10/19

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Keywords

  • False Data Injection
  • Long Short Term Memory (LSTM)
  • Random and Target attacks
  • State Estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Kundu, A., Sahu, A., Davis, K., & Serpedin, E. (2019). Learning-Based Defense of False Data Injection Attacks in Power System State Estimation. In 51st North American Power Symposium, NAPS 2019 [9000216] (51st North American Power Symposium, NAPS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS46351.2019.9000216