Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (8): 260-270.doi: 10.11896/jsjkx.221000103

• Information Security • Previous Articles     Next Articles

Attack Economics Based Fraud Detection for MVNO

LI Yang1, LI Zhenhua1, XIN Xianlong2   

  1. 1 School of Software,Tsinghua University,Beijing 100084,China
    2 Xiaomi Technology Co. LTD.,Beijing 100085,China
  • Received:2022-10-13 Revised:2023-03-03 Online:2023-08-15 Published:2023-08-02
  • About author:LI Yang,born in 1996,Ph.D candidate.His main research interests include network measurement and data mining.
    LI Zhenhua,born in 1983,Ph.D,asso-ciate professor,Ph.D supervisor,is a senior member of China Computer Fe-deration.His main research interests include mobile network measurement and virtualization technology.
  • Supported by:
    National Key R & D Program of China(2022YFB4500703) and National Natural Science Foundation of China(61902211,62202266).

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Abstract: Driven by the full utilization of telecommunication resources and stimulating healthy market competition,mobile virtual network opera-tors(MVNOs) become popular rapidly in recent years.MVNOs rely on the infrastructures of mobile network ope-rators(MNOs) to provide users with cheaper and more flexible services.Due to the high maintenance costs of physical stores,MVNOs mostly provide fully online service.However,scammers use vulnerabilities in online authentication to purchase SIM cards and make scam calls,which seriously affects the reputation of MVNOs and their users.This has become a bottleneck problem for the survival and development of MVNOs.To address this issue,we collaborate with a large commercial MVNO with over 2 million users named Xiaomi Mobile.Related work generally assumes that scam calls are random,scattered or hidden,ma-king the detection methods inefficient or even invalid for the scenario of MVNOs.However,by analyzing the crowdsourced dataset,almost all scam calls are found to be organized,planned,and scaled.Thus,a method based on attack economics and reasonable analysis of the spatio-temporal characteristics of scam calls is proposed.This method successfully extracts the key features,and by combining with machine learning-based classification,it greatly reduces the proportion of scammers in Xiaomi Mobile to 0.023‰,which is far lower than the 0.1‰ achieved by the MNOs that have sufficient information.Under the premise of excluding the risk of being cracked,part of the code and data has been open sourced to help purify the ecology of entire telecom industry.

Key words: MVNO, Fraud detection, Attack economics, Spatio-Temporal analysis, Machine learning

CLC Number: 

  • TP393
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