Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, Netherlands.
Institute of Criminal Law and Criminology, Leiden University, Steenschuur 25, 2311 ES, Leiden, Netherlands.
Sci Rep. 2024 Jul 16;14(1):16336. doi: 10.1038/s41598-024-67115-5.
In this work we focus on identifying key players in dark net cryptomarkets that facilitate online trade of illegal goods. Law enforcement aims to disrupt criminal activity conducted through these markets by targeting key players vital to the market's existence and success. We particularly focus on detecting successful vendors responsible for the majority of illegal trade. Our methodology aims to uncover whether the task of key player identification should center around plainly measuring user and forum activity, or that it requires leveraging specific patterns of user communication. We focus on a large-scale dataset from the Evolution cryptomarket, which we model as an evolving communication network. Results indicate that user and forum activity, measured through topic engagement, is best able to identify successful vendors. Interestingly, considering users with higher betweenness centrality in the communication network further improves performance, also identifying successful vendors with moderate activity on the forum. But more importantly, analyzing the forum data over time, we find evidence that attaining a high betweenness score comes before vendor success. This suggests that the proposed network-driven approach of modelling user communication might prove useful as an early warning signal for key player identification.
在这项工作中，我们专注于识别暗网加密货币市场中的关键参与者，这些参与者促进了非法商品的在线交易。执法部门旨在通过针对对市场的存在和成功至关重要的关键参与者来扰乱通过这些市场进行的犯罪活动。我们特别关注检测负责大部分非法交易的成功供应商。我们的方法旨在揭示关键参与者的识别任务是否应该集中在简单地衡量用户和论坛活动上，还是需要利用用户通信的特定模式。我们专注于来自 Evolution 加密货币市场的大规模数据集，我们将其建模为一个不断发展的通信网络。结果表明，通过主题参与衡量的用户和论坛活动最能够识别成功的供应商。有趣的是，考虑到在通信网络中具有较高中介中心性的用户可以进一步提高性能，同时也可以识别出在论坛上活动适度的成功供应商。但更重要的是，随着时间的推移分析论坛数据，我们发现有证据表明，获得高中介得分先于供应商的成功。这表明，所提出的基于网络的用户通信建模方法可能被证明是识别关键参与者的早期预警信号有用。
