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The anonymity on the Darknet allows vendors to stay undetected by usingmultiple vendor aliases or frequently migrating between markets. Consequently,illegal markets and their connections are challenging to uncover on theDarknet. To identify relationships between illegal markets and their vendors,we propose VendorLink, an NLP-based approach that examines writing patterns toverify, 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 performclosed-set vendor verification, open-set vendor identification, andlow-resource market adaption tasks. Through VendorLink, we uncover (i) 15migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17migrants 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 moreinformed decisions by verifying and identifying migrating vendors and theirpotential aliases on existing and Low-Resource (LR) emerging Darknet markets.

VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets