An autoencoder-based network intrusion detection system for the scada system

Mustafa Altaha, Jae Myeong Lee, Muhammad Aslam, Sugwon Hong

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


—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.

Original languageEnglish
Pages (from-to)210-216
Number of pages7
JournalJournal of Communications
Issue number6
StatePublished - Jun 2021


  • Autoencoder
  • Cybersecurity
  • Deep learning
  • DNP3
  • Network intrusion detection system


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