On the Generation of Anomaly Detection Datasets in Industrial Control Systems
| Autor: | Ruben Mendez Nistal, Félix J. García Clemente, Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Carlos Javier Del Canto Masa, Cristian Cadenas Sarmiento, Alberto Huertas Celdrán |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science Feature extraction 02 engineering and technology Anomaly detection computer.software_genre 0202 electrical engineering electronic engineering information engineering General Materials Science critical infrastructures business.industry Deep learning industrial control systems General Engineering 020206 networking & telecommunications industry applications Industrial control system machine learning Software deployment industrial control 020201 artificial intelligence & image processing Data mining Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 177460-177473 (2019) |
| ISSN: | 2169-3536 |
| Popis: | In recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people’s safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems. |
| Databáze: | OpenAIRE |
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