  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.
