Detection of False Data Injection Attacks in Smart Microgrid using Deep Learning to Improve Classification Algorithms-Reference-Cited by-同舟云学术

Detection of False Data Injection Attacks in Smart Microgrid using Deep Learning to Improve Classification Algorithms

Published:2023-11-02 Issue: Volume:18 Page:196-201
ISSN:
Container-title:2023 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS)
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Author:

Shahinzadeh Hossein¹,Gharehpetian Gevork B.¹,Yaïci Wahiba²,Afzoon Fatemeh³,Karimi Ali⁴,Ebrahimi Farshad⁵

Affiliation:

1. Amirkabir University of Technology,Department of Electrical Engineering,Tehran,Iran

2. Natural Resources Canada,CanmetENERGY Research Centre,Ottawa,Canada

3. K. N. Toosi University of Technology,Department of Electrical Engineering,Tehran,Iran

4. Georgia Institute of Technology,Dept. of Electrical and Computer Engineering,Atlanta,GA,USA

5. University of Houston,Dept. of Electrical and Computer Engineering,Houston,TX,USA,77004

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424028/10424019/10424213.pdf?arnumber=10424213

Reference26 articles.

1. Resilient Configuration of Distribution System versus False Data Injection Attacks Against State Estimation;Behnam;Iranian Journal of Electrical & Electronic Engineering,2022

2. Resilience Assessment of Distribution Systems Against Extreme Weather Events: Flooding Threats in Iran's Electricity Network

3. Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

4. An Agile Black-Out Detection and Response Paradigm in Smart Grids Incorporating IoT-Oriented Initiatives and Fog-Computing Platform

5. Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts