Machine Learning for Reliable Network Attack Detection in SCADA Systems

Abstract

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F 1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F 1 score of respectively > 99%. mehr

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Titel	Machine Learning for Reliable Network Attack Detection in SCADA Systems
Medien	17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications, 2018 (IEEE TrustCom)
Verlag	IEEE
ISBN	978-1-5386-4389-1
Verfasser	Rocio Lopez Perez, Prof. Dr. Florian Adamsky, Ridha Soua, Thomas Engel
Veröffentlichungsdatum	06.09.2018
Zitation	Perez, Rocio Lopez; Adamsky, Florian; Soua, Ridha; Engel, Thomas (2018): Machine Learning for Reliable Network Attack Detection in SCADA Systems. 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications, 2018 (IEEE TrustCom). DOI: 10.1109/TrustCom/BigDataSE.2018.00094