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
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Input
Devices : Keyboard, Mouse
·
Ram : 1
GB
SOFTWARE
REQUIREMENTS:
·
Operating
system : Windows 7.
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Coding
Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database
: MYSQL
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.