2021/12/29 by Nuno Oliveira, Norberto Sousa, Oliveira, Nuno +5
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.2112.14821
openalex publication_date 2021/12/29 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/30
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.