Mass registration is the foundation of most sophisticated online attacks. Facebook and Twitter recently disclosed as many as 270M and 48M accounts on their respective platforms are fake. Fraudsters start by mass registering an army of accounts and then camouflaging them with real-looking user activity. When these accounts are later used for an attack, they are much harder to detect with existing solutions. DataVisor’s Unsupervised Machine Learning Engine is uniquely capable of detecting these mass-registered accounts because it uncovers the hidden connections between accounts, even if those accounts have not yet done any damage or started their attack. This allows companies to quarantine or add extra authentication steps to suspicious accounts and stop them before they strike.
How Attackers Mass Register Accounts
Fake User Activity
Attackers simulate user activity by uploading stolen photos and content from other sites, making them appear real even to human reviewers.
Attackers use readily-available stolen credentials or information from data breaches to create authentic-looking new accounts.
Proxies, VPNs, and cloud-hosting services allow attackers to evade IP or location blacklists and digital-fingerprint solutions.
How Unsupervised Machine Learning Stops Mass Registration
There are many challenges when it comes to mass registration detection. For one, the amount of data is limited at registration. Further, falsely rejecting a real customer at account opening can prevent a legitimate person from signing up with the service. DataVisor’s Unsupervised Machine Learning Engine looks at a new registration in the context of millions of recent registrations, deriving and analyzing a rich array of features, in order to determine if there are any suspicious similarities between the newly registered accounts. This allows the UML Engine to adapt in real-time as fraudsters change their attack techniques, keeping your online service free of fake accounts and the downstream havoc they attempt to conduct.
Detect malicious intent at point of registration, preventing downstream damage
Accuracy and Coverage
Analyze hidden connections between accounts to detect more attacks while lowering false positives.
Unknown Threat Detection
Uncover new and evolving attack patterns without any training data or labels.
Learn More About How DataVisor Stops Mass Registration
Watch this recording of a webinar hosted by Julian Wong, Technical Architect at DataVisor, and his special guest, Jim Blomo, Director of Engineering at Yelp, to learn how Yelp stops fake users while connecting people with local businesses.
The DataVisor Platform
Unsupervised Machine Learning Engine
Supervised Machine Learning Engine
Automated Rules Engine
Global Intelligence Network
Aggregate and analyze the industry’s broadest array of digital fingerprints and signals from billions of users across a variety of industries.
What’s Happening with Mass Registration
The DataVisor Online Fraud Report took a look at our base of more than one billion users across 172+ countries in the world. Using this massive amount of data, we were able to identify some of the favorite tools and attack techniques that online criminals from around the globe favor when doing their dirty work.
Wondering if your company has any crime rings sleeping among your users? Most will acknowledge that there are likely some accounts lurking here or there, but may not realize that it’s a big problem.
Fake accounts are a bigger problem than ever. With so many new security technologies, why are they still so prevalent? Recent studies show that approximately 10 percent of accounts on social media sites are fake.