Open Access

ARTICLE

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

by Wen Deng¹, Guangjun Liang^1,2,3,*, Chenfei Yu¹, Kefan Yao¹, Chengrui Wang¹, Xuan Zhang¹

1 Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, China
2 Engineering Research Center of Electronic Data Forensics Analysis, Nanjing, China
3 Department of Public Security of Jiangsu Province, Key Laboratory of Digital Forensics, Nanjing, China

* Corresponding Author: Guangjun Liang. Email:

Computers, Materials & Continua 2023, 75(1), 1561-1576. https://doi.org/10.32604/cmc.2023.035016

Received 03 August 2022; Accepted 08 December 2022; Issue published 06 February 2023

Abstract

With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people. However, due to the lack of an effective preventive mechanism, the police are often in a passive position. Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data. First, we perform preliminary screening and cleaning based on the keyword “defrauded” to obtain valid fraudulent user Identity Documents (IDs). The basic information and account information of these users is user-labeled to achieve the purpose of distinguishing the types of fraud. Secondly, through feature engineering technologies such as avatar recognition, Artificial Intelligence (AI) sentiment analysis, data screening, and follower blogger type analysis, these pictures and texts will be abstracted into user preferences and personality characteristics which integrate multi-dimensional information to build user portraits. Third, deep neural network training is performed on the cube. 80% percent of the data is predicted based on the N-way K-shot problem and used to train the model, and the remaining 20% is used for model accuracy evaluation. Experiments have shown that Few-short learning has higher accuracy compared with Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN). On this basis, this paper develops a WeChat small program for early warning of telecommunications network fraud based on user portraits. When the user enters some personal information on the front end, the back-end database can perform correlation analysis by itself, so as to match the most likely fraud types and give relevant early warning information. The fraud warning model is highly scaleable. The data of other Applications (APPs) can be extended to further improve the efficiency of anti-fraud which has extremely high public welfare value.

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Keywords

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