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

Authors: Nuno Oliveira, Norberto Sousa, Jorge Oliveira, Isabel Praça
Published: 2021-12-29

Source: https://arxiv.org/abs/2112.14821

PDF: https://arxiv.org/pdf/2112.14821

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機械学習技術 評価手法 データ収集

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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