# Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space

arXiv: https://arxiv.org/abs/2112.14821 · HTML: https://mlsanity.com/paper/2112.14821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2021.

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

- [Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector](https://mlsanity.com/paper/2207.01595/) (2022)

## Abstract (arXiv)

Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an F1-score of 87.89% in the SWaT dataset.