A Reinforcement Learning Approach to Undetectable Attacks Against Automatic Generation Control-Reference-Cited by-同舟云学术

A Reinforcement Learning Approach to Undetectable Attacks Against Automatic Generation Control

Published:2024-01 Issue:1 Volume:15 Page:959-972
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Shereen Ezzeldin¹^ORCID,Kazari Kiarash¹^ORCID,Dán György¹^ORCID

Affiliation:

1. Division of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

Funder

Swedish Civil Contingencies Agency (MSB) through the CERCES2 Project

Swedish Research Council

Swedish Foundation for Strategic Research through the CLAS Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10372481/10159364.pdf?arnumber=10159364

Reference70 articles.

1. Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

2. Delayed Observers for Linear Systems With Unknown Inputs

3. Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)

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