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<p>The adoption of the Industrial Internet of Things (IIoT) as a complementary technology to Operational Technology (OT) has enabled a new level of standardised data access and process visibility. This convergence of Information Technology (IT), OT, and IIoT has also created new cybersecurity vulnerabilities and risks that must be managed. Artificial Intelligence (AI) is emerging as a powerful tool to monitor OT/IIoT networks for malicious activity and is a highly active area of research. AI researchers are applying advanced Machine Learning (ML) and Deep Learning (DL) techniques to the detection of anomalous or malicious activity in network traffic. They typically use datasets derived from IoT/IIoT/OT network traffic captures to measure the performance of their proposed approaches. Therefore, there is a widespread need for datasets for algorithm testing. This work systematically reviews publicly available network traffic capture-based datasets, including categorisation of contained attack types, review of metadata, and statistical as well as complexity analysis. Each dataset is analysed to provide researchers with metadata that can be used to select the best dataset for their research question. This results in an added benefit to the community as researchers can select datasets more easily and according to specific Machine Learning goals.</p>

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Systematic review and characterisation of malicious industrial network traffic datasets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月15日

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  • Martin Dobler,
  • Michael Hellwig,
  • Nuno Lopes,
  • Ken Oakley,
  • Mike Winterburn

摘要

The adoption of the Industrial Internet of Things (IIoT) as a complementary technology to Operational Technology (OT) has enabled a new level of standardised data access and process visibility. This convergence of Information Technology (IT), OT, and IIoT has also created new cybersecurity vulnerabilities and risks that must be managed. Artificial Intelligence (AI) is emerging as a powerful tool to monitor OT/IIoT networks for malicious activity and is a highly active area of research. AI researchers are applying advanced Machine Learning (ML) and Deep Learning (DL) techniques to the detection of anomalous or malicious activity in network traffic. They typically use datasets derived from IoT/IIoT/OT network traffic captures to measure the performance of their proposed approaches. Therefore, there is a widespread need for datasets for algorithm testing. This work systematically reviews publicly available network traffic capture-based datasets, including categorisation of contained attack types, review of metadata, and statistical as well as complexity analysis. Each dataset is analysed to provide researchers with metadata that can be used to select the best dataset for their research question. This results in an added benefit to the community as researchers can select datasets more easily and according to specific Machine Learning goals.