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

作     者：Wen Deng Guangjun Liang Chenfei Yu Kefan Yao Chengrui Wang Xuan Zhang 

作者机构：Department of Computer Information and Cyber SecurityJiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics AnalysisNanjingChina Department of Public Security of Jiangsu ProvinceKey Laboratory of Digital ForensicsNanjingChina 

出 版 物：《计算机、材料和连续体（英文）》 (Computers, Materials, & Continua)

年 卷 期：2023年第75卷第4期

页      面：1561-1576页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 0303[法学-社会学] 0710[理学-生物学] 0401[教育学-教育学] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Jiangsu Province Big Data Management Center State Key Laboratory of Nanjing University “Research on Intrusion Signal Detection Technology Based on Deep Learning in Complex Electromagnetic Environment Zhejiang University, ZJU State Key Laboratory of Computer Aided Design and Computer Graphics 

主　　题：Crawler user portrait feature engineering deep learning small program development 

摘      要：With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch 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 whichhas extremely high public welfare value.