DRAGON: Detection of Related Account Groups for Online services with uncertain graphs
Journal
2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
Date Issued
2021
Author(s)
URI
Abstract
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. © 2021 IEEE.
Subjects
account identity relation; suspicious account detection; uncertain graph
Other Subjects
'current; Account identity relation; Modeling uncertainties; On-line service; Physical devices; Real-world; Real-world datasets; Service provider; Suspicious account detection; Uncertain graphs
Type
conference paper