Intrusion Detection Datasets for IIoT and ICS: A Taxonomic Review with a Decision-Aid Scoring Rubric
Ayman Termanini, Hadj Bourdoucen, Dawood Al-Abri, Ahmed Al Maashri · 2026
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Description
Ayman Termanini and colleagues present a 2026 review that scores the datasets used to train intrusion detectors for industrial control systems and connected factory gear. Detector quality hinges on training data, yet surveys usually compare sets in words only. This team graded 23 public datasets on seven measured axes, mapped their attacks to the standard MITRE playbook for industrial systems, and built a decision rubric to help buyers choose.
Most sets come from physical test rigs, a point for realism, and nearly all map to the attack playbook. Gaps show too: one factory protocol dominates while others are scarce, and multi-stage attack stories are rare. The authors advise pairing a realism-strong set with a reproducibility-strong one.
Catalog record
| CREATOR | Ayman Termanini, Hadj Bourdoucen, Dawood Al-Abri, Ahmed Al Maashri |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 81798 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |