Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space
Abstract
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.
Keywords
Cite
@article{arxiv.2112.14821,
title = {Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space},
author = {Nuno Oliveira and Norberto Sousa and Jorge Oliveira and Isabel Praça},
journal= {arXiv preprint arXiv:2112.14821},
year = {2022}
}