# Benchmark Evaluation of Anomaly-Based Intrusion Detection Systems in the Context of Smart Grids

arXiv: https://arxiv.org/abs/2312.13705 · HTML: https://mlsanity.com/paper/2312.13705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2023.

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

- [Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids](https://mlsanity.com/paper/2510.02371/) (2025)

## Abstract (arXiv)

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.