Paper Info
Title
BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group Detection.
Abstract
Frauds severely hurt many kinds of Internet businesses. Group-based fraud detection is a popular methodology to catch fraudsters who unavoidably exhibit synchronized behaviors. We combine both graph-based features (e.g. cluster density) and information-theoretical features (e.g. probability for the similarity) of fraud groups into two intuitive metrics. Based on these metrics, we build an extensible fraud detection framework, BadLink, to support multimodal datasets with different data types and distributions in a scalable way. Experiments on real production workload, as well as extensive comparison with existing solutions demonstrate the state-of-the-art performance of BadLink, even with sophisticated camouflage traffic.
Year
Venue
Field
2018
arXiv: Cryptography and Security
Group detection,Graph,Computer science,Workload,Theoretical computer science,Data type,Camouflage,Artificial intelligence,Machine learning,Scalability,The Internet
DocType
Volume
Citations 
Journal
abs/1805.10053
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yikun Ban101.35
Xin Liu204.06
Tianyi Zhang318.46
Ling Huang42496118.80
Yitao Duan523118.87
Xue Liu601.01
Wei Xu765641.71