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Incorporating user behavior flow for user risk assessment

Yuxiang Shan (Department of Information Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China)
Qin Ren (Department of Information Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China)
Gang Yu (Department of Information Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou, China)
Tiantian Li (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China)
Bin Cao (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 5 July 2023

Issue publication date: 12 July 2023

71

Abstract

Purpose

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Design/methodology/approach

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Findings

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

Originality/value

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Keywords

Acknowledgements

This work was partially supported by following funds: Research Project of China Tobacco Zhejiang Industrial Co., Ltd under Grant No. ZJZY2022E005, and Zhejiang Provincial Natural Science Foundation of China (Youth) under Grant No. LQ21F020019. Authors also acknowledge here the data processing work by postgraduate students from Zhejiang University of Technology (China): Kaibo He, Wei Cai, Guanda Chen, Hao Wen and Chenwen Ma.

Citation

Shan, Y., Ren, Q., Yu, G., Li, T. and Cao, B. (2023), "Incorporating user behavior flow for user risk assessment", International Journal of Web Information Systems, Vol. 19 No. 2, pp. 80-101. https://doi.org/10.1108/IJWIS-02-2023-0025

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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