ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents

• URL: http://arxiv.org/abs/2305.09476v1
• Date: Fri, 21 Apr 2023 11:36:18 GMT
• Title: ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents
• Authors: Thomas Wolgast, Nils Wenninghoff, Stephan Balduin, Eric Veith, Bastian Fraune, Torben Woltjen, Astrid Nie{\ss}e
• Abstract summary: ANALYSE is a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems. It is designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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