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.
@article{arxiv.2305.09476,
title = {ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents},
author = {Thomas Wolgast and Nils Wenninghoff and Stephan Balduin and Eric Veith and Bastian Fraune and Torben Woltjen and Astrid Nieße},
journal= {arXiv preprint arXiv:2305.09476},
year = {2024}
}