ANALYSE--Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents
Wolgast, Thomas and Wenninghoff, Nils and Balduin, Stephan and Veith, Eric and Fraune, Bastian and Woltjen, Torben and Nieße, Astrid
SoftwareX
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.
04 / 2023
article
Pyrate
Polymorphic agents as cross-sectional software technology for the analysis of the operational safety of cyber-physical systems
Polymorphic agents as cross-sectional software technology for the analysis of the operational safety of cyber-physical systems
