Mass Registration

Sophisticated online attacks begin well before fraudulent activity occurs or damage is done. 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.

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How Fraudsters 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.

Device Obfuscation

Fraudsters utilize mobile device flashing and virtual machines to appear as if they are registering from many different devices.

Stolen Identities

Attackers use readily-available stolen credentials or information from data breaches to create authentic-looking new accounts.

IP Obfuscation

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.

Early Detection

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.

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Gain unprecedented insight into the behaviors and techniques fraudsters use to evade detection.

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Momo uses DataVisor’s Detection Solution to detect mass account registration and account takeovers.

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Learn how Yelp stops fake accounts with DataVisor’s Unsupervised Machine Learning.

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The DataVisor Detection Solution

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.

Supervised Machine Learning Engine

Use industry leading supervised machine learning algorithms to augment the unsupervised machine learning detection with client-provided labels.

Automated Rules Engine

Generate and deprecate rules automatically, lowering maintenance costs and improving results explainability.

DataVisor 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.

Account linkage view to discover hidden links among malicious accounts

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What’s Happening With Mass Registration


How to Register Millions of Fake Accounts with Ease

Fake accounts are a bigger problem than ever. With so many new security, why are they still so prevalent? Recent studies show that approximately 10 percent of accounts on social media sites are fake. Other reports are more drastic: Instagram’s crackdown on spam fake accounts in 2014 exposed 29 percent of followers of the Instagram official account as fake.

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Infographic: The Online Fraudster’s Tool Shed

DataVisor releases a new infographic highlighting the tools fraudsters use to build their armies of attackers. From their email addresses to their use of cloud services, we know what they are doing when they are DIY-ing an attack.

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Twitter Bots: These are the Droids You’re Looking For

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. This attitude is held by a lot of companies, large and small, and many think they have it under control. Think again.

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Getting Started

Want to get started and find out how DataVisor can help find malicious accounts hiding inside your online service? Request a security assessment today!

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