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Detecting compromised accounts caused by phone number recycling on E-commerce platforms: Taking Meituan as an example
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Min GAO, Shutong CHEN, Yangbo GAO, Zhenhua ZHANG, Yu CHEN, Yupeng LI,Qiongzan YE, Xin WANG, Yang CHEN. Detecting compromised accounts caused by phone number recycling on E-commerce platforms: Taking Meituan as an example[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Detecting compromised accounts caused by phone number recycling on E-commerce platforms: Taking Meituan as an example",
author="Min GAO, Shutong CHEN, Yangbo GAO, Zhenhua ZHANG, Yu CHEN, Yupeng LI,Qiongzan YE, Xin WANG, Yang CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300291"
}
%0 Journal Article
%T Detecting compromised accounts caused by phone number recycling on E-commerce platforms: Taking Meituan as an example
%A Min GAO
%A Shutong CHEN
%A Yangbo GAO
%A Zhenhua ZHANG
%A Yu CHEN
%A Yupeng LI
%A Qiongzan YE
%A Xin WANG
%A Yang CHEN
%J Journal of Zhejiang University SCIENCE C
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300291
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A1 - Yupeng LI
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DOI - 10.1631/FITEE.2300291
Abstract: phone number recycling (PNR) refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner. It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms. Specifically, a new owner of a reassigned number can access the application account that the number is associated with, and may perform fraudulent activities. Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users. Thus, alternative solutions that depend on only the information of the applications are imperative. In this work, we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers. Our analysis on Meituan’s real-world dataset shows that compromised accounts have unique statistical features and temporal patterns. Based on the observations, we propose a novel model called Temporal pattern and Statistical feature Fusion model (TSF) to tackle the problem, which integrates a Temporal Pattern Encoder and a Statistical Feature Encoder to capture behavioral evolutionary interaction and significant operation features. Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the compared baselines, which demonstrates its effectiveness in detecting compromised accounts due to reassigned numbers.
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