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 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第4期1561-1576,共16页计算机、材料和连续体（英文）

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.

关 键 词：CRAWLER user portrait feature engineering deep learning small program development 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]