Automated acquisition and analysis of illegal fund accounts in gambling websites

Abstract

While the rapid growth of the internet has made life more convenient, it has also made it easier for people to access online gambling sites. This has led to an increase in fraud, resulting in significant financial losses and serious societal issues. However, the abundance of gambling sites, diverse development frameworks, and complex identity verification methods pose significant challenges to gaining comprehensive access to their illegal funds. To address online gambling fraud and assist law enforcement in cutting off their funding chains, we investigate gambling websites to uncover information about the physical funding accounts used by criminal groups. Given the extensive scale of these websites, we propose an auto-registration framework based on You Only Look Once version 4 (YOLOv4) to automate account registration and retrieve illegal fund account details. Additionally, we construct a dataset of user interface (UI) elements from 17 types of gambling websites for model training. The YOLOv4 model achieves an F1-score of 0.84 and a mean Average Precision (mAP) of 83.96%. The proposed framework achieves a registration success rate of 60.58%. After extracting data from numerous gambling websites in seven batches, we identify 7496 entity account details and 47 payment methods, analyze the wealth of entity information, and highlight six new payment methods. Finally, by identifying recurring illegal fund accounts across multiple domains, we confirm 23 criminal gangs, providing substantial support to law enforcement agencies in combating online gambling-related crimes.