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.

翻译：暗网的匿名性使卖家能够通过使用多个化名或在市场间频繁迁移来规避侦查。因此，非法市场及其关联关系在暗网上难以被发现。为识别非法市场与其卖家之间的关系，我们提出VendorLink——一种基于自然语言处理的方法，通过分析七个公开暗网市场的文本广告中的写作模式，验证、识别并关联唯一的卖家账号。与现有研究不同，VendorLink利用监督预训练的优势，执行闭集卖家验证、开集卖家识别及低资源市场自适应任务。通过VendorLink，我们在Alphabay-Dreams-Silk数据集中发现（i）15个迁移者与71个潜在化名，在Valhalla-Berlusconi数据集中发现（ii）17个迁移者与3个潜在化名，以及在Traderoute-Agora数据集中发现（iii）75个迁移者与10个潜在化名。总体而言，我们的方法通过验证和识别现有及低资源新兴暗网市场中迁移的卖家及其潜在化名，能够帮助执法机构做出更明智的决策。