Fraud Detection in Dynamic Interaction Network

doi:10.1109/TKDE.2019.2912817

Fraud Detection in Dynamic Interaction Network

2020-10-01

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DOI：10.1109/TKDE.2019.2912817

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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.

Keywords:

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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