Hornet 40: Network Dataset of Geographically Placed Honeypots
Bibliographic record
Abstract
Hornet 40 is a dataset of 40 days of network traffic attacks captured in cloud servers used as honeypots to help understand how geography may impact the inflow of network attacks. The honeypots are located in eight different cities: Amsterdam, London, Frankfurt, San Francisco, New York, Singapore, Toronto, Bangalore. The data was captured in April, May, and June 2021. The eight cloud servers were created and configured simultaneously following identical instructions. The network capture was performed using the Argus network monitoring tool in each cloud server. The cloud servers had only one service running (SSH on a non-standard port) and were fully dedicated as a honeypot. No honeypot software was used in this dataset. The dataset consists of eight scenarios, one for each geographically located cloud server. Each scenario contains bidirectional NetFlow files in the following format: - hornet40-biargus.tar.gz: all scenarios with bidirectional NetFlow files in Argus binary format; - hornet40-netflow-v5.tar.gz: all scenarios with bidirectional NetFlow v5 files in CSV format; - hornet40-netflow-extended.tar.gz: all scenarios with bidirectional NetFlows files in CSV format containing all features provided by Argus. - hornet40-full.tar.gz: download all the data (biargus, NetFlow v5, and extended NetFlows)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".