ANALYZING AND DETECTING MONEY LAUNDERING ACCOUNTS IN ONLINE SOCIAL NETWORKS

Virtual money in OSNs plays an expanding essential job in supporting different budgetary exercises, for example, cash trade, online shopping, and paid amusements. Clients more often than not buy virtual money utilizing genuine cash. This reality motivates assailants to instrument a multitude of records to gather virtual cash deceptively or wrongfully with no or exceptionally ease and afterward launder the gathered virtual cash for monstrous benefit. Such assaults not just present huge monetary loss of unfortunate casualty clients, yet in addition hurt the practicality of the biological system. It is in this manner of focal significance to identify pernicious OSN accounts that participate in washing virtual money. To this end, we broadly think about the conduct of both malignant and kindhearted records dependent on task information gathered from Tencent QQ, one of the biggest OSNs on the planet. At that point, we devise multi-faceted highlights that portray accounts from three viewpoints: account reasonability, exchange successions, and spatial relationship among records. At long last, we propose a discovery technique by coordinating these highlights utilizing a factual classifier, which can accomplish a high recognition rate of 94.2 percent at an exceptionally low false positive rate of 0.97 percent.