Real-CATS

About this source

Real-CATS is the Real Criminal Addresses for Targeted Studies dataset, published as part of an academic Bitcoin AML study. It collects ~90K Bitcoin addresses associated with criminal activity (Ransomware, Blackmail Scam, Darknet Market, Mixer, Theft, Tumbler) with category labels suitable for machine-learning training. We ingest only the criminal addresses (CB.tsv); the matched-benign sample (BB.tsv, 90K generic clean addresses) is intentionally not loaded because blanket benign labels add noise to /address attribution.

How we got the data

Cloned from sjdseu/Real-CATS on GitHub. CB.tsv parsed at load time; per-row category preserved as the badge on /address pages.

Why this confidence

Research-derived, varies in citation quality. The dataset's value is in its breadth, but individual entries inherit the uncertainty of whichever upstream feed the authors aggregated. Lower than direct-disclosure (1.0) and curated WalletExplorer (0.85) but useful as corroboration for OFAC / OpenSanctions / WatchYourBack overlap.

License & attribution

Academic redistribution, citation requested. We retain the dataset citation in our docs/SOURCES.md; please cite the original authors if you use this data downstream.

Sample addresses

Up to 100 representative rows from this source, picked live from labels.db. Addresses appearing in the current rich-list snapshot are surfaced first, sorted by balance. Click any address to see its full /address aggregator entry.