ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents
This addresses security vulnerabilities in energy systems for operators and policymakers, though it appears incremental as it builds on existing literature.
The authors tackled the problem of malicious attacks in cyber-physical energy systems by proposing ANALYSE, a machine-learning-based software suite that enables learning agents to autonomously find attacks, resulting in the ability to discover unknown attack types and reproduce known strategies.
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.