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 Nieße
Published: 2023-04-21

Source: https://arxiv.org/abs/2305.09476

PDF: https://arxiv.org/pdf/2305.09476

AIにより推定されたラベル

強化学習 攻撃シナリオ分析 サイバー攻撃

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

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.