Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space
This work addresses security for critical infrastructures like airports and hospitals, but it appears incremental as it builds on existing AI methods with optimization enhancements.
The paper tackles the problem of anomaly detection in cyber-physical systems by proposing a framework based on error space reconstruction with genetic algorithm hyperparameter optimization, achieving an F1-score of 87.89% on the SWaT dataset.
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.