ANALYSE — Learning to attack cyber–physical energy systems with intelligent agents

Author:

Wolgast ThomasORCID,Wenninghoff Nils,Balduin Stephan,Veith Eric,Fraune Bastian,Woltjen Torben,Nieße Astrid

Funder

Bundesministerium fur Bildung und Forschung Dienststelle Berlin

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Computer Science Applications,Software

Reference24 articles.

1. A review of cyber–physical energy system security assessment;Rasmussen,2017

2. Analyzing power grid, ict, and market without domain knowledge using distributed artificial intelligence;Veith,2020

3. Towards reinforcement learning for vulnerability analysis in power-economic systems;Wolgast;Energy Inform,2021

4. A reinforcement learning approach for sequential decision-making process of attacks in smart grid;Ni,2018

5. Q-learning-based vulnerability analysis of smart grid against sequential topology attacks;Yan;IEEE Trans Inf Forensics Secur,2017

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Imitation Game: A Model-Based and Imitation Learning Deep Reinforcement Learning Hybrid;2024 12th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES);2024-05-13

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