  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.
