Protecting Social Network Users from Impersonation

Abstract

Malicious actors on social networks often create fake accounts that impersonate legitimate users in order to carry out scams, send spam and phishing messages, or engage in other abusive behavior. The act of assuming a real person»s identity gives the bad actor an additional level of trust that can make the attack more likely to succeed. In addition to the direct damage caused by the fraudulent activity, impersonation erodes users» trust in the platform and damages the brand of the legitimate user being impersonated. In this paper, we present a large-scale system that identifies and takes action on impersonation profiles on a social network. The system is composed of three tiers of protection: (1) an offline system that protects all users by searching for profile data duplicity combined with suspicious signals; (2) an offline system with stricter logic that proactively protects high-profile users in population segments that have historically been targeted for impersonation; and (3) an online system that provides low-latency protection to a manually curated list of individuals that have been actively targeted by impersonation. The tiers build on one another with decreasing detection latency and more targeted user coverage. We have deployed our system on the professional social network LinkedIn and drastically reduced the extent of impersonation abuse on LinkedIn. Specifically, we observed a reduction in the impersonator median time-to-detect of more than $98%$, a more than fourfold improvement in recall (as measured on a monthly basis) and reduction over $95%$ in the number of impersonation reports by the most targeted company on LinkedIn.