@article{wolgast2023analyse,
Author = {Wolgast, Thomas and Wenninghoff, Nils and Balduin, Stephan and Veith, Eric and Fraune, Bastian and Woltjen, Torben and Nieße, Astrid},
Title = {ANALYSE--Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents},

Journal = {SoftwareX},
Year = {2023},
Month = {04},
Doi = {10.1016/j.softx.2023.101484},
Url = {https://ui.adsabs.harvard.edu/abs/2023SoftX..2301484W/abstract},
type = {article},
Abstract = {The ongoing penetration of energy systems with information and communications technology (ICT) and the introduction of new markets increase the potential for malicious or profit-driven attacks that endanger system stability. To ensure security-of-supply, it is necessary to analyze such attacks and their underlying vulnerabilities, to develop countermeasures and improve system design. We propose ANALYSE, a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems, consisting of the power system, ICT, and energy markets. ANALYSE is a modular, configurable, and self-documenting framework designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature. }
}
@COMMENT{Bibtex file generated on }