# ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents

arXiv: https://arxiv.org/abs/2305.09476 · HTML: https://mlsanity.com/paper/2305.09476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems](https://mlsanity.com/paper/1811.06447/) (2018)
- [Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence](https://mlsanity.com/paper/2006.06074/) (2020)

## Built on by (most cited first, 1 of 1)

- [Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid](https://mlsanity.com/paper/2404.01794/) (2024)

## Abstract (arXiv)

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.