Problem
Research questions and friction points this paper is trying to address.
Bitcoin
Illicit Addresses
Dataset Reproducibility
Evidence-backed Labeling
Cybercrime
Extracting and Verifying Illicit Bitcoin Addresses from Underground Forum Discussions
🤖 AI Summary
This study addresses the irreproducibility and lack of direct evidence in existing Bitcoin illicit labeling datasets by proposing an evidence-driven label construction paradigm. Integrating large language model screening, expert review, and on-chain transaction verification, we establish a fully transparent pipeline for extracting and validating illicit addresses from underground forums. We release a comprehensive dataset comprising 2,438 manually verified illicit addresses alongside complete code tools. This contribution effectively resolves the challenge of missing evidentiary support, significantly enhancing both dataset credibility and research reproducibility in cryptocurrency forensics.
📝 Abstract
Existing labeled Bitcoin datasets are largely derived from community-reported abuse, blockchain heuristics, incident-specific collections, or proprietary labeling processes. Their construction methods are rarely publicly reproducible and often provide limited evidence that an address was directly involved in illicit activity. We present a reproducible pipeline for constructing evidence-backed Bitcoin labels from HackForums, an underground cybercrime forum with fifteen years of archived activity. The pipeline combines LLM-assisted screening, expert review, and on-chain validation to identify Bitcoin addresses explicitly associated with illicit transactions discussed on the forum. Each released label is supported by contextual evidence from underground discussions and validated on-chain. The resulting dataset contains 2,438 manually verified illicit Bitcoin addresses spanning 2010-2024 and twelve cybercrime categories assigned during LLM screening. We release the dataset, temporal metadata, and the complete extraction pipeline to support reproducible research on cryptocurrency-facilitated cybercrime.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reproducible Pipeline
LLM-assisted Screening
Evidence-backed Labels
On-chain Validation
Underground Forum Mining
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