Identifying Fraudsters and Fraudulent Strategies in Mobile Social Network

Abstract

Modern communication technologies have developed quickly, especially communications through (mobile) phones, which has greatly aided in the sharing of information and social connections between people. Nonetheless, the rise of telemarketing scams has the potential to seriously deplete communal and private wealth, slowing down or harming the economy. With an emphasis on exposing the "precise fraud" phenomena and the techniques employed by fraudsters to precisely choose targets, we propose to identify telemarketing scams in this study. We utilise a one-month comprehensive dataset of telecommunication information from Shanghai, which includes 698 million call logs and 54 million customers, to explore this issue. During our research, we have discovered that user information may have been substantially compromised, and that fraudsters prefer to target users who are younger and more active on mobile networks. To further separate fraudsters from non-fraudsters, we provide a unique semi-supervised learning approach. Our technique beats various cutting-edge algorithms in terms of accuracy of identifying fraudsters, according to experimental findings on real-world data. We think that our research may help governments and mobile service providers make better policy decisions.