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Fraud rings don’t have to fuss with all the mundane details of running a business — the scam is the business.
It’s that tidy business model that has enabled a new e-commerce threat group to leave its mark in November with what one researcher calls the largest attack of its kind in the past 20 years.
And they’re just getting started.
The particularly prolific Southeast Asian-based e-commerce threat group has been able to build up a sophisticated operation stacked with data science, fraud detection, online payments, and e-commerce expertise that so far has enabled them to rip off an estimated $660 million in stolen laptops, cell phones, computer chips, gaming devices, and more in November, according to a new report from Signifyd researchers.
The threat actors use stolen credentials and account takeover to place orders from unsuspecting consumers’ accounts, often using stored payment methods. Then, they re-ship them to Asia for repackaging and resale at a premium. According to a tandem report earlier this month on the ring, the group uses mules to do the dirty work of reshipment, often under duress.
“Additionally, if the MSHT (Modern Slavery & Human Trafficking) connections that have appeared can be confirmed, this fraud ring also manipulates people to coerce them to become part of the attack,” according to that analysis, from Chargelytics Consulting.
In all, the group targeted a massive $3.3 billion worth of e-commerce merchandise during November, the busiest shopping month of the year, according Signifyd’s team, which has been following the group’s illicit activities for more than a year.
Holiday Season Scam ‘War’
“What was unique about this fraud ring was that they revved up really quickly. They’re fast and strong,” said Ping Li, Signifyd vice president of risk and chargeback operations at Signifyd, in its report this week. “They probably had been preparing for it for a long time, and then they launched a war just before our holiday season.”
Li, who has studied how to stop e-commerce fraud for two decades, ranks this attack as the most dangerous he’s ever seen, because of its ability to attempt large numbers of fraudulent transactions per minute, which in one case Signifyd analysts observed kept up for a full day.
“Normally, when we see an attack on one merchant, the attack has its own characteristics. And then you see a very different kind of attack on another merchant,” Li said. “But this one is just universal. It’s everywhere. This is the first time I have seen an attack of this size and scale in our network.”
The scammers are also apparently not concerned about being caught. “They kind of leave their signature,” Li said. “They are not really trying to hide. It’s like, ‘Catch me if you can.'”
Excellence in E-Commerce Fraud
Besides the operation being stacked with technology know-how, Michael Pezely, Signifyd’s director of risk intelligence, tells Dark Reading that the e-commerce threat group has sheer speed and volume of scam transactions on its side.
“E-commerce orders — particularly at the enterprise level — arrive at dizzying speed,” Pezely says. “Signifyd, for instance, processed as much as $42 million an hour in orders during Cyber Week. It would be virtually impossible for a human team to review that volume of orders for signs of fraud.”
Pezely added that merchants are on the lookout for goods being shipped to a foreign country, but this group of scammers places orders that appear to originate from the US and ship to US addresses.
“Furthermore, if a merchant is relying on only its own transaction data, there likely will be a lag between the time a fraud attack begins and when it is recognized,” Pezely explains. “Without having the benefit of seeing millions of transactions across thousands of merchants, a novel fraud attack might not be in plain sight for some time.”
Automation Is Part of the Answer
His recommendation to e-commerce security teams is that they need to rely on a combination of automation and machine learning informed by patterns across the broader online retail sector.
“And so, automation is part of the answer — in particular, machine learning solutions that are able to recognize patterns and associate them with known bad actors and bad events, while constantly improving their performance to suppress new attacks,” Pezely explains.
He adds, “To be effective, teams also need to rely on large networks of many merchants, which provide the transaction intelligence that allows machine learning models to identify attack patterns at one merchant and adjust protection across the network to avoid losses among other merchants on the network.”
Once the models are created, it’s up to human expertise to put the data together and create a plan for cyber-defense.
Merchants would do well to get ahead of the threat, given the billions of dollars in goods already in the crosshairs of this lone e-commerce fraud ring, Pezely advises.
“Given that a fraud ring’s cost of inventory is zero, there is plenty of room to plot future endeavors,” he says.
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