The anonymity on the Darknet allows vendors to stay undetected by using
multiple vendor aliases or frequently migrating between markets. Consequently,
illegal markets and their connections are challenging to uncover on the
Darknet. To identify relationships between illegal markets and their vendors,
we propose VendorLink, an NLP-based approach that examines writing patterns to
verify, identify, and link unique vendor accounts across text advertisements
(ads) on seven public Darknet markets. In contrast to existing literature,
VendorLink utilizes the strength of supervised pre-training to perform
closed-set vendor verification, open-set vendor identification, and
low-resource market adaption tasks. Through VendorLink, we uncover (i) 15
migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17
migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii)
75 migrants and 10 potential aliases in the Traderoute-Agora dataset.
Altogether, our approach can help Law Enforcement Agencies (LEA) make more
informed decisions by verifying and identifying migrating vendors and their
potential aliases on existing and Low-Resource (LR) emerging Darknet markets.

提供了一种基于 NLP 的方法 VendorLink，通过模式识别可以在 Darknet 找到并验证多个供应商帐户，帮助执法机构更好地识别潜在的非法市场和供应商。