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A Multiperspective Fraud Detection Method for Multi-Participant E-commerce Transactions.pdf (1.6 MB)

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

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journal contribution
posted on 2023-06-12, 14:30 authored by W Yu, Y Wang, L Liu, Y An, B Yuan, J Panneerselvam

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Funding

10.13039/501100007128-Natural Science Foundation of Shaanxi Province (Grant Number: 2021JM-205) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 52172325) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: 300102242902)

History

Author affiliation

School of Informatics, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Computational Social Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2329-924X

eissn

2329-924X

Copyright date

2023

Available date

2023-06-12

Language

English