Fraud Detection in Dynamic Interaction Network

Hao Lin; Guannan Liu; Junjie WuYuan ZuoXin WanHong Li

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Fraud Detection in Dynamic Interaction Network

机译：Fraud Detection in Dynamic Interaction Network

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摘要

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov Model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.

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《IEEE Transactions on Knowledge and Data Engineering》 |2020年第10期|1936-1950|共15页
作者
Hao Lin; Guannan Liu; Junjie WuYuan ZuoXin WanHong Li;
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作者单位

School of Economics and Management, Beihang University, Beijing, China;

National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China;

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原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
Hidden Markov models; Anomaly detection; Data models; Probabilistic logic; Correlation; Telecommunications; fraud detection; sequential schema; interaction schema; telecommunication network; probabilistic graphical model;

Fraud Detection in Dynamic Interaction Network

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