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

TL;DR

This paper introduces a novel anomaly detection framework for cyber-physical systems that reconstructs error spaces and uses genetic algorithms for hyperparameter tuning, achieving high accuracy on a benchmark dataset.

Contribution

It presents a new error space reconstruction approach combined with genetic algorithms for hyperparameter optimization in anomaly detection.

Findings

Achieved an F1-score of 87.89% on the SWaT dataset.

Demonstrated effectiveness of the proposed method in detecting anomalies.

Enhanced anomaly detection accuracy through hyperparameter optimization.

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.