CloudAPT: a benchmark dataset to evaluate APT countermeasures in cloud environments
Bibliographic record
Abstract
In the evolving cybersecurity landscape, Advanced Persistent Threats (APTs) targeting cloud environments pose significant risks to organizations and governments that rely on cloud services. Recent research contributions address critical issues and advance the state-of-the-art, leveraging datasets generated from non-cloud environments. As a result, existing datasets are often inadequate for developing and evaluating robust APT detection mechanisms in cloud contexts. We present a novel benchmark dataset designed to reproduce APT activities in a cloud environment, leveraging a Kubernetes cluster that mirrors the infrastructure used by small to mid-sized organizations. The dataset is generated over eight days covering the entire cloud APT attack lifecycle, including reconnaissance, initial compromise, privilege escalation, lateral movement, and data exfiltration. This dataset provides valuable resources for researching and developing advanced APT countermeasures, featuring interactions from multiple real users while a human attacker conducts malicious activities. The CloudAPT dataset aims to empower researchers to improve cloud security through advanced analytical solutions (e.g., using machine learning).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.116 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".