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CMU Algorithm Detects Online Fraudsters

By Carnegie Mellon News (PA)

September 12, 2016

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Carnegie Mellon University (CMU) researchers say they have developed an algorithm called FRAUDAR that can perceive fraudsters hiding behind a digital veneer of legitimacy.

CMU professor Christos Faloutsos says FRAUDAR analyzed Twitter data for 41.7 million users and 1.47 billion followers to detect more than 4,000 accounts not previously tagged as fraudulent, including many that employed known follower-buying services. "We're not identifying anything criminal here, but these sorts of frauds can undermine people's faith in online reviews and behaviors," Faloutsos says.

FRAUDAR first locates accounts it can confidently rate as legitimate and then eliminates them until it drills down to a bipartite core. The researchers randomly chose 125 followers and 125 followees from the suspect group, and two control groups of 100 users who had not been picked out by FRAUDAR. They examined each for links associated with malware or scams and for robot-like behavior, and found 57% of followers and 40% of followees in the suspicious group were labeled as fraudulent, versus 12% and 25%, respectively, in the control groups.

A paper detailing FRAUDAR won the Best Paper Award in August at the ACM Conference on Knowledge Discovery and Data Mining (KDD 2016) in San Francisco.

From Carnegie Mellon News (PA)
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