The increasing digitization of smart grids has made addressing cybersecurity issues crucial in order to secure the power supply. Anomaly detection has emerged as a key technology for cybersecurity in smart grids, enabling the detection of unknown threats. Many research efforts have proposed various machine-learning-based approaches for anomaly detection in grid operations. However, there is a need for a reproducible and comprehensive evaluation environment to investigate and compare different approaches to anomaly detection. The assessment process is highly dependent on the specific application and requires an evaluation that considers representative datasets from the use case as well as the specific characteristics of the use case. In this work, we present an evaluation environment for anomaly detection methods in smart grids that facilitates reproducible and comprehensive evaluation of different anomaly detection methods.

翻译：智能电网日益数字化，使得解决网络安全问题以确保电力供应安全变得至关重要。异常检测已崛起为智能电网网络安全的关键技术，能够检测未知威胁。许多研究提出了基于机器学习的方法用于电网运行的异常检测。然而，目前亟需一个可重复且全面的评估环境，以研究和比较不同的异常检测方法。评估过程高度依赖于具体应用场景，需考虑用例的代表性数据集及其特定特征。在本工作中，我们提出了一种智能电网异常检测方法的评估环境，该环境支持对不同异常检测方法进行可重复且全面的评估。