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.

翻译：暗网市场因其匿名性为非法商品与服务的交易提供了大型平台。基于用户在不同市场发布的帖子学习其不变表示，有助于跨平台聚合用户信息，从而识别匿名用户。传统用户表示方法主要依赖对帖子文本信息的建模，无法捕获帖子的时间内容与论坛交互特征。近期研究虽多采用CNN建模帖子文本信息，却难以有效处理因篇幅频繁变化的帖子序列。针对上述问题，我们提出名为URM4DMU（面向暗网市场用户的用户表示模型）的模型，该模型通过增强卷积算子与自适应门控机制的自注意力机制改进帖子表示，在融合帖子时间内容与论坛交互信息后表现更优。我们在四个暗网市场上验证了URM4DMU的有效性，其MRR值与Recall@10指标相较当前最优方法分别平均提升22.5%与25.5%。