A Deep Learning Game Theoretic Model for Defending Against Large Scale Smart Grid Attacks-Reference-Cited by-同舟云学术

A Deep Learning Game Theoretic Model for Defending Against Large Scale Smart Grid Attacks

Published:2023-03 Issue:2 Volume:14 Page:1188-1197
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Cunningham James D.¹,Aved Alexander²^ORCID,Ferris David²,Morrone Philip²,Tucker Conrad S.³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

2. Information Exploitation Division, Air Force Research Laboratory, Rome, NY, USA

3. Department of Mechanical Engineering and the Machine Learning, Robotics, Biomedical Engineering Departments, Carnegie Mellon University, Pittsburgh, PA, USA

Funder

Griffith Institute and Air Force Research Lab

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/10049220/09858166.pdf?arnumber=9858166

Reference32 articles.

1. Reinforcement-learning-based dynamic defense strategy of multistage game against dynamic load altering attack

2. Reinforcement-learning-based dynamic defense strategy of multistage game against dynamic load altering attack

3. Maximizing Resilience under Defender Attacker Model in Heterogeneous Multi-Networks

4. Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

5. Deep reinforcement learning from self-play in imperfect-information games;heinrich;arXiv 1603 01121,2016

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