A Topic-Based Unsupervised Learning Approach for Online Underground Market Exploration - Huang, Shin-Ying; Ban, Tao (IEEE ... International Conference on Trust, Security and Privacy in Computing and Communications (Online), 2019)
Cyber fraud has become a lucrative form of illicit business by leveraging the Internet as a communication channel and as a result, causes significant losses to the economy. Criminals in the cyber fraud underground economy use online underground markets and other forms of social media to exchange and trade illegitimate information. Due to the high variability in the marketplaces and actors therein, analyzing these underground markets is challenging. To understand more about the underground economy of cyber fraud and its actors, we propose a topic-based hierarchical self-organizing map, which can well represent and visualize actors' similarity and thus, uncover their roles in the underground markets. We compare the proposed method with a topic-based social network analysis method for identifying the key users and their roles in the cyber fraud value chain. Experiments conducted on data from several online underground markets suggest that the proposed method can aid in identifying key actors in terms of roles, influence levels, and their social relationships.