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Researchers Uncover Network of 15,000 Crypto Scam Bots on Twitter




Recently published research has explained the notorious occurrence of cryptocurrency-related Twitter accounts promoting fake “giveaways,” unveiling a network of at least 15,000 scam bots.

The examination of Twitter bots, which continue to annoy and even fool unwary users, was done by cybersecurity firm Duo Security.

The project involved a massive 88 million Twitter accounts, with researchers making use of machine learning techniques to train a bot classifier.

Using just the most recent 200 tweets from each account, the classifier uncovered a mesh of 15,000 bots that were hard at work disseminating fake competitions and mimicking some of the cryptocurrency industry’s best-known figures and businesses.

“Users are likely to trust a tweet more or less depending on how many times it’s been retweeted or liked. Those behind this particular botnet know this, and have designed it to exploit this very tendency,” Duo data scientist Olabode Anise said in a press release.

Those who have been victimized by the scams’ attempts at identity theft have since changed their Twitter handles to caution others that they were not giving away coins or tokens.

For those who are active on “crypto Twitter,” the bots have become part of the scene, because of the ubiquity of their fake campaigns.

Despite their huge numbers, Duo found, the bots are also actively engaged in avoiding being shut down.

“The bots’ tries to elude detection demonstrate the significance of taking a close look at an account holistically, including the metadata around the content,” Anise explained:

“For example, bot accounts will typically tweet in short bursts, causing the average time between tweets to be very low. Documenting these patterns of behavior can also be used to identify other malicious and spam botnets.”

Anise and another researcher, principal R&D engineer Jordan Wright, would present their results at the 2018 Black Hat USA security conference in Las Vegas this Wednesday, August 8.

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