Darknet markets provide a large platform for trading illicit goods and
services due to their anonymity. Learning an invariant representation of each
user based on their posts on different markets makes it easy to aggregate user
information across different platforms, which helps identify anonymous users.
Traditional user representation methods mainly rely on modeling the text
information of posts and cannot capture the temporal content and the forum
interaction of posts. While recent works mainly use CNN to model the text
information of posts, failing to effectively model posts whose length changes
frequently in an episode. To address the above problems, we propose a model
named URM4DMU(User Representation Model for Darknet Markets Users) which mainly
improves the post representation by augmenting convolutional operators and
self-attention with an adaptive gate mechanism. It performs much better when
combined with the temporal content and the forum interaction of posts. We
demonstrate the effectiveness of URM4DMU on four darknet markets. The average
improvements on MRR value and Recall@10 are 22.5% and 25.5% over the
state-of-the-art method respectively.

提出了一种名为 URM4DMU 的模型，用于改进卷积操作和自注意力机制，并与时间内容和论坛交互相结合来改进 用户在暗网市场的表示方法，从而帮助识别匿名用户。在四个暗网市场上，相比于其他最先进的暗网用户表示方法，该模型能够显著提高 MRR 值和 Recall@10 的性能。