URM4DMU: an user represention model for darknet markets users
This work addresses the challenge of user identification in darknet markets for law enforcement or security applications, representing an incremental improvement over existing methods.
The paper tackles the problem of identifying anonymous users across darknet markets by learning invariant user representations from posts, achieving average improvements of 22.5% in MRR and 25.5% in Recall@10 over state-of-the-art methods.
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.