A case study detailing a large botnet of at least 15,000 bots spreading a cryptocurrency scam. By monitoring the botnet over time, we discover ways the bots evolve to evade detection.
Our cryptobot scam case study demonstrates that, after finding initial bots using the tools and techniques described in this paper, a thread can be followed that can result in the discovery and unraveling of an entire botnet. For this botnet, we use targeted social network analysis to reveal a unique three-tiered hierarchical structure.
https://duo.com/blog/dont-me-hunting-twitter-bots-at-scaleDamn, 15,000 bots spreading scams all over Twitter, this is just a tip of the iceberg, but I'm sure that once they bots are banned, others bots will born, much better and sophisticated and evolves that's makes it hard to identify. And this bots knows how to clone real crypto accounts that if you gullible enough, you might be persuaded by this bot and put money and before you knew, the scammers are gone taking everything along the way.