voidly-anomaly-dbscan-v1

emperor-mew/voidly-anomaly-dbscan-v1

voidly-anomaly-dbscan-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: 2026-05-21T04:19:40.634338Z | 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: 5dc14bce

Model card

From the Hugging Face model README

Voidly Atlas Unsupervised Anomaly (CenDTect-style DBSCAN) v1

Version: v1 | Trained: 2026-05-21T04:19:40.634338Z | License: CC BY 4.0

Per-country rolling 45-day window, DBSCAN(eps=75th-pct kNN, min_samples=3) over 12 standardized OONI features. Promoted as a second-opinion signal — the supervised v3.3 classifier still dominates at AUC 0.99; DBSCAN surfaces shape-anomalous days that labels never saw.

Eval

Metric	Value
`auc`	0.6506
`auc_pr`	0.3639
`precision_at_p90`	0.4326
`recall_at_p90`	0.1662
`n_scored`	3922
`n_positive`	1023
`n_negative`	2899
`promote_floor_auc`	0.6500
`window_days`	45

Features

block_rate
log_n
pct_dns_block
pct_tcp_reset
pct_blockpage
pct_tls_reset
pct_outage
pct_interference
pct_block
asn_unique_log
source_diversity
asn_unique_normalized

Honest caveats

AUC 0.65 — just above the 0.65 promote floor. Not a primary signal.
Use as a second opinion alongside the supervised v3.3 classifier (AUC 0.99).
Labels are imperfect ground truth — DBSCAN may surface real anomalies that don't have a label.

Reproducibility

Paper: Aceto & Pescape 2025 — CenDTect (https://arxiv.org/abs/...)

Citation

@misc{voidly_voidly_anomaly_dbscan_v1,
  title  = {Voidly Atlas: voidly-anomaly-dbscan-v1 (v1)},
  author = {Voidly},
  year   = {2026},
  url    = {https://huggingface.co/emperor-mew/voidly-anomaly-dbscan-v1},
  note   = {Open censorship-research ML stack. CC BY 4.0.}
}

Method foundation: CenDTect (Aceto & Pescape 2025)

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