ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916177087823872 |
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| author | Wolgast, Thomas Wenninghoff, Nils Balduin, Stephan Veith, Eric Fraune, Bastian Woltjen, Torben Nieße, Astrid |
| author_facet | Wolgast, Thomas Wenninghoff, Nils Balduin, Stephan Veith, Eric Fraune, Bastian Woltjen, Torben Nieße, Astrid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_09476 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents Wolgast, Thomas Wenninghoff, Nils Balduin, Stephan Veith, Eric Fraune, Bastian Woltjen, Torben Nieße, Astrid Cryptography and Security Machine Learning Systems and Control 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. |
| title | ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents |
| topic | Cryptography and Security Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2305.09476 |