Evidence map›Paper›PMID 41778228›Full record

ArticleData in brief2026

Windows-APT 2025: A dataset for APT-inspired attack scenarios on windows systems.

Maryam Mozaffari, Abbas Yazdinejad, Ali Dehghantanha

Abstract read
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maryam MozaffariCyber Science Lab (CSL), Guelph, ON, Canada.
Abbas YazdinejadAIMMLab, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Ali DehghantanhaCyber Science Lab (CSL), Guelph, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Windows-APT Dataset 2025 represents a significant advancement in cybersecurity research, addressing critical gaps in the understanding of advanced persistent threat (APT) tactics against Windows systems. Existing datasets largely focus on network data, often overlooking the detailed tactics, techniques, and procedures (TTPs) used by sophisticated threat actors. To bridge this gap, we developed a comprehensive dataset of 36 APT-inspired scenarios derived from threat actor profiles documented in the MITRE ATT&CK framework. Scenario selection mirrors MITRE ATT&CK group entries reported as China-attributed; we do not assert attribution and focus strictly on reproducing reported TTPs for research. Leveraging the MITRE Caldera framework for adversary emulation, we generated extensive system and network event logs, collected via Wazuh, and systematically mapped them to the MITRE ATT&CK framework. This dataset provides a valuable asset for machine learning model training, intrusion detection system evaluation, and the enhancement of APT dynamics studies. By providing a detailed view of APT activities in Windows environments, it enables stronger threat detection, informs defensive strategies, and facilitates development of effective countermeasures against emerging cyber threats. The dataset package contains 19 CSV files (including 16 per-period logs, one combined log, and two supplementary CSVs for manifest and validation), along with configuration files to support exact replication.

Indexed as

Advanced Persistent ThreatCybersecurity DatasetIntrusion DetectionMITRE ATT&CKTactics Techniques and Procedures

Identifiers

PMID41778228
PMCPMC12950481

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.