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An Autoencoder-Based Network Intrusion Detection System for the SCADA System

Mustafa Altaha 1, Jae-Myeong Lee 1, Muhammad Aslam 2, and Sugwon Hong 1
1. Dept. of Computer Engineering, Myongji University, Yongin, R. of Korea
2. Dept. of Electrical Engineering, Myongji University, Yongin, R. of Korea

Abstract—The intrusion detection system (IDS) is the main tool to do security monitoring that is one of the security strategies for the supervisory control and data acquisition (SCADA) system. In this paper, we develop an IDS based on the autoencoder deep learning model (AE-IDS) for the SCADA system. The target SCADA communication protocol of the detection model is the Distributed Network Protocol 3 (DNP3), which is currently the most commonly utilized communication protocol in the power substation. Cyberattacks that we consider are data injection or modification attacks, which are the most critical attacks in the SCADA systems. In this paper, we extracted 17 data features from DNP3 communication, and use them to train the autoencoder network. We measure accuracy and loss of detection and compare them with different supervised deep learning algorithms. The unsupervised AE-IDS model shows better performance than the other deep learning IDS models.
Index Terms—Network intrusion detection system, DNP3, SCADA, autoencoder, deep learning, cybersecurity

Cite: Mustafa Altaha, Jae-Myeong Lee, Muhammad Aslam, and Sugwon Hong, "An Autoencoder-Based Network Intrusion Detection System for the SCADA System," Journal of Communications vol. 16, no. 6, pp. 210-216, June 2021. Doi: 10.12720/jcm.16.6.210-216

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.