Work

Research on Intelligent Classification Algorithm for Telecom Network Fraud Related Individuals

Year: 2025

Type: conference-paper

Abstract: This study analyzes telecom network fraud cases using the K-means clustering algorithm and the Chi-squared Automatic Interaction detection (CHAID) decision tree algorithm to identify the characteristics of victims and suspects and the factors influencing the amount of fraud. Through clustering analysis of victim and suspect attributes, it was found that there are five main types of victims, while suspects involved in network fraud are generally young and have a low level of education. In the decision tree analysis, it was found that the monthly income of victims has the greatest impact on the amount of fraud, followed by the education level and criminal history of the suspects. Based on these results, it can be predicted that victims with lower education levels and higher monthly incomes are more likely to experience higher amounts of fraud, while suspects who have been exposed to the internet for a longer time and have lower education levels are more likely to engage in criminal activities.

Source: International Conference on Image Processing, Machine Learning and Pattern Recognition (IPMLPR)

DOI: 10.1145/3759928.3759954

Authors: Hao Chu, Ronghe Wang, Yongtao Wei, Liwei Liu

Institutions: China Electronics Technology Group Corporation, China Academy of Electronics and Information Technology

Language: English

Topic: Cybercrime and Law Enforcement Studies

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Funder: National Natural Science Foundation of China