People have long been aware of malicious users that impersonate celebrities or launch identity theft attacks in social networks. However, beyond anecdotal evidence, there have been no in-depth studies of impersonation attacks in today's social networks. One reason for the lack of studies in this space is the absence of datasets about impersonation attacks. To this end, we propose a technique to build extensive datasets of impersonation attacks in current social networks and we gather 16,572 cases of impersonation attacks in the Twitter social network. Our analysis reveals that most identity impersonation attacks are not targeting celebrities or identity theft. Instead, we uncover a new class of impersonation attacks that clone the profiles of ordinary people on Twitter to create real-looking fake identities and use them in malicious activities such as follower fraud. We refer to these as the doppelganger bot attacks. Our findings show (i) that identity impersonation attacks are much broader than believed and can impact any user, not just celebrities and (ii) that attackers are evolving and create real-looking accounts that are harder to detect by current systems. We also propose and evaluate methods to automatically detect impersonation attacks sooner than they are being detected in today's Twitter social network.
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