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May 18, 2026Cureus Journal of Computer Science.Open Access

Performance Enhancement of Supervisory Control and Data Acquisition (SCADA) IEC 60870-5-104 Intrusion Detection Using Sequence-Aware and Hybrid Deep Learning Models: A Comparative Evaluation

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Authors

MAM. Agus Syamsul ArifinJenderal Soedirman UniversityDSDeris StiawanSSSusanto Susanto

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Implication

Randomized trial evaluates performance of deep learning models in detecting attacks in SCADA systems, suggesting optimal cybersecurity solutions.

Key Points

  • This study compares deep learning models for effective intrusion detection in SCADA IEC 60870-5-104 networks.
  • Analyzed various deep learning architectures including BiLSTM and CNN-based hybrids.
  • Utilized a dataset from a realistic SCADA testbed comprising benign and attack traffic.
  • Evaluated model performance using accuracy, precision, recall, F1-score, and confusion matrices.
  • BiLSTM and LSTM-GRU models achieved the highest detection rates across most attack classes.
  • CNN-based hybrids performed poorly for certain attack types like ICMP flood, despite high accuracy.
  • Overall, sequence-based deep learning models showed superior effectiveness for SCADA cybersecurity.

Cite This Study

Arifin et al. (2026) studied this question.

synapsesocial.com/papers/6a0aabf55ba8ef6d83b6f8b2https://doi.org/10.7759/s44389-026-00076-0
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