![]() ![]() The models score the clusters, then propagate the cluster label to individual accounts. These are supervised machine learning models that use features per cluster instead of per member. We then find account clusters that show a statistically abnormal distribution of data, which is indicative of being created or controlled by a single bad actor. First, we create clusters of accounts by grouping them based on shared attributes. For this reason, we have other downstream models to catch smaller batches of fakes. The figure below shows one attack where the model blocked five million fake accounts from being created in less than a day.Īlthough we prevent a large majority of bulk fake accounts from being created at registration, we sometimes don’t have enough information at that point to determine if accounts are fake. This registration model is quite effective at preventing bulk fake account creation. Attempts with medium risk scores are challenged by our security measures to verify that they are real people. Signup attempts with a low abuse risk score are allowed to register right away, while attempts with a high abuse risk score are prevented from creating an account. Thus, in order to proactively stop fake accounts at scale, we have machine-learned models to detect groups of accounts that look or act similarly, which implies they were created or controlled by the same bad actor.Įvery new user registration attempt is evaluated by a machine-learned model that gives an abuse risk score. For many types of abuse, attackers require a large number of fake accounts for the attack to be financially feasible. We aim to catch the majority of fake accounts as quickly as possible to prevent harm to our members.Īt the top of the funnel is the first line of defense: registration scoring. In order to build robust countermeasures against different types of attacks on our platform, we employ a funnel of defenses to detect and take down fake accounts at multiple stages. By preventing or promptly removing fake accounts on the site, we ensure that LinkedIn members are protected. Fake profiles can be used to carry out many different types of abuse: scraping, spamming, fraud, and phishing, among others. There is a wide range of sophistication behind these bad actors and the intent varies. Unfortunately, LinkedIn is the target of bad actors who constantly try to create fake accounts. The Anti-Abuse team at LinkedIn creates the systems that allow us to protect our members from activity by bad actors. One of the ways we ensure that accounts are real is by building automated detection systems at scale for detecting and taking action against fake accounts. To maintain a safe and trusted professional community on LinkedIn, we require that every LinkedIn profile must uniquely represent a real person.
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