TSE-Tran:Prediction method of telecommunication-network fraud crime based on time series representation and transformer  

作　　者：Tuo Shi Jie Fu Xiaofeng Hu 

机构地区：[1]Department of Public Security Management,Beijing Police College,Beijing 102202,China [2]Environmental Security Defense Brigade,Beijing Municipal,Public Security Bureau Miyun Branch,Beijing 101599,China [3]School of Information Technology and Cyber Security,People’s Public Security University of China,Beijing 100038,China

出　　处：《Journal of Safety Science and Resilience》2023年第4期340-347,共8页安全科学与韧性（英文）

摘　　要：Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience.Therefore,preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system.Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns.This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran,which integrates temporal representation and transformer architecture.The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module,which maps the sequence data to a more precise feature representation tensor that accounts for both intra-and inter-cycle features.Subsequently,the data are fed into the transformer-encoder module for further encoding,capturing long-range dependencies in the time-series data.Finally,occurrences of future telecommunication network frauds are predicted by a fully connected layer.The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy.The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.

关 键 词：Urban resilience TimesNet TRANSFORMER Time series prediction Attention mechanism 

分 类 号：D924.35[政治法律—刑法学] TP39[政治法律—法学]