DRAGON: Detection of Related Account Groups for Online services with uncertain graphs
Bing-Jyue Chen, Wun-Cing Liou, Hsing-Yu Shih, Tsung-Nan Lin · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
With the rising influence of current online services, it is important for service providers to discover related accounts because it helps detect suspicious account groups. Existing research on this topic mostly focuses on a variety of account behaviors. Little attention has been paid to relations among account identity, which unveils the relationship between accounts and real-world people. In this paper, we propose DRAGON for modeling an account identity network and detecting suspicious account groups among this network. To this end, identifiers for tracking physical devices are collected and uncertain graph is used for modeling uncertainty in the network. Within this network, a strategy for detecting suspicious account groups is also investigated in DRAGON. We evaluate DRAGON using a real-world dataset. The results indicate that DRAGON achieves a 280% improvement in precision and 150% improvement in recall compared to a binary classifier.