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This study compares deep learning models for effective intrusion detection in SCADA IEC 60870-5-104 networks.

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

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.

Arifin et al. (2026) studied this question.

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View Full Paper

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Authors

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

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.

Arifin et al. (2026) studied this question.

View Full Paper

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