Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks
Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

ABSTRACT:

Virtual currency in OSNs plays an increasing­ly important role in supporting various financial activities such as currency exchange, online shop­ping, and paid games. Users usually purchase virtual currency using real currency. This fact moti­vates attackers to instrument an army of accounts to collect virtual currency unethically or illegally with no or very low cost and then launder the collected virtual money for massive profit. Such attacks not only introduce significant financial loss of victim users, but also harm the viability of the ecosystem. It is therefore of central importance to detect malicious OSN accounts that engage in laundering virtual currency. To this end, we extensively study the behavior of both malicious and benign accounts based on operation data collected from Tencent QQ, one of the largest OSNs in the world. Then, we devise multi-faceted features that characterize accounts from three aspects: account viability, transaction sequences, and spatial correlation among accounts. Finally, we propose a detection method by integrating these features using a statistical classifier, which can achieve a high detection rate of 94.2 percent at a very low false positive rate of 0.97 percent.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Yadong Zhou, Ximi Wang, Junjie Zhang, Peng Zhang, Lili Liu, Huan Jin, and Hongbo Jin, “Analyzing and Detecting Money-Laundering Accounts in Online Social Networks”, IEEE Network , Volume: 32, Issue: 3, May/June 2018