APPLICATION OF SUPPORT VECTOR MACHINE AND CONTEXTUAL OUTLIERS FOR INTRUSION DETECTION IN THE SCADA SYSTEM

Nguyễn Văn Xuân; Vũ Đức Trường; Nguyễn Mạnh Hùng; Nguyễn Tăng Cường

APPLICATION OF SUPPORT VECTOR MACHINE AND CONTEXTUAL OUTLIERS FOR INTRUSION DETECTION IN THE SCADA SYSTEM

About this article

Received: 27/08/19 Revised: 22/09/19 Published: 03/10/19

Authors

1. Nguyen Van Xuan , Faculty of Control Engineering – Military Technical Academy
2. Vu Duc Truong, Faculty of Control Engineering – Military Technical Academy
3. Nguyen Manh Hung, Faculty of Control Engineering – Military Technical Academy
4. Nguyen Tang Cuong, Faculty of Control Engineering – Military Technical Academy

Abstract

In this paper, we present an IDA-SCADA model based on Support Vector Machine (SVM) which is capable of detecting intrusion into SCADA systems with high accuracy. The distinction of our method used in this research is we applied contextual training data. To do that, the original dataset was reorganized to create context before training the SVM phase. The result of our work is the proposed system able to identify any attacks or normal patterns with precision from 95.02% to 99.03%.

Keywords

Intrusion detection system, Machine Learning, IDS, SVM, SCADA.

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