voidly-anomaly-domain-drift-v1

emperor-mew/voidly-anomaly-domain-drift-v1

voidly-anomaly-domain-drift-v1 is a machine learning model from emperor-mew. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for scikit-learn. The card lists the license as cc-by-4.0.

Version: v1 | Trained: 2026-05-21T04:43:38.216279Z | License: CC BY 4.0

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Updated May 22, 2026

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At a glance

Library: scikit-learn
License: cc-by-4.0
Access: Public
Created: May 22, 2026
Updated: May 22, 2026
SHA: 8f7c0b0c

Model card

From the Hugging Face model README

Voidly Atlas Domain Drift HDBSCAN v1

Version: v1 | Trained: 2026-05-21T04:43:38.216279Z | License: CC BY 4.0

Weekly per-domain HDBSCAN drift surface. Variable-density clustering over the last-28-day feature vector for every domain with >=10 measurements. Orthogonal axis to per-country DBSCAN.

Eval

Metric	Value
`algorithm`	HDBSCAN(min_cluster_size=5, metric=euclidean, after StandardScaler)
`min_cluster_size`	5
`n_domains_this`	27
`n_clusters_this`	2
`n_new_clusters`	2
`n_high_drift_clusters`	0

Features

log_n
avg_block_rate
std_block_rate
n_countries_blocking
asn_unique_log
source_diversity
pct_dns_block
pct_tcp_reset
pct_blockpage
pct_tls_reset
pct_isp_outage
cat_NEWS
cat_ANON
cat_GRP
cat_PORN
cat_MSG
cat_SRCH
cat_OTHER

Honest caveats

Only 27 domains pass the min-10-measurement filter — week-over-week cluster stability needs months more data.
Drift score is L2 distance in this-week's standardized space; domains absent from one week get status=new_this_week / dropped_this_week instead of a score.
ORTHOGONAL to per-country DBSCAN (voidly-anomaly-dbscan-v1) — different axis, different surface.

Citation

@misc{voidly_voidly_anomaly_domain_drift_v1,
  title  = {Voidly Atlas: voidly-anomaly-domain-drift-v1 (v1)},
  author = {Voidly},
  year   = {2026},
  url    = {https://huggingface.co/emperor-mew/voidly-anomaly-domain-drift-v1},
  note   = {Open censorship-research ML stack. CC BY 4.0.}
}

Method foundation: McInnes et al. 2017 — arXiv:1705.07321

Library: scikit-learn
License: cc-by-4.0
Created: May 22, 2026
Updated: May 22, 2026