Novelty Detection System Based on Multi-criteria Evaluation in Respect of Industrial Control System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)

Abstract

The industrial processes and systems have become more sophisticated and also adopted in diverse areas of human activities. The Industrial Control System (ICS) or Internet of Things (IoT) have become essential for our daily life, and therefore vital for contemporary society. These systems are often included in Critical Information Infrastructure (CII) which is crucial for each state. Consequently, the cyber defense is and will be one of the most important security field for our society. Therefore, we use the novelty detection approach in order to identify anomalies which can be a symptom of the cyber-attack in ICS environment. To achieve the main goal of the article One-Class Support Vector Machine (OCSVM) algorithm was used. Moreover, the anomaly detection algorithm is adjusted via multi-criteria evaluation and classifier fusion.

Keywords

Cyber security Novelty detection Anomaly detection Industrial control systems Multi-criteria evaluation 

Notes

Acknowledgments

This work was funded by the Internal Grant Agency (IGA/FAI/2018/003) and supported by the project ev. no. VI20152019049 “RESILIENCE 2015: Dynamic Resilience Evaluation of Interrelated Critical Infrastructure Subsystems”, supported by the Ministry of the Interior of the Czech Republic in the years 2015–2019 and also supported by the research project VI20172019054 “An analytical software module for the real-time resilience evaluation from point of the converged security”, supported by the Ministry of the Interior of the Czech Republic in the years 2017-2019. Moreover, this work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. Finally, we thank our colleagues from Mississippi State University and Oak Ridge National Laboratory which provides SCADA datasets.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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