Transforms static IP data and behavior patterns to uncover and identify potential fraud and security threats.
Neustar's UtraReputation is the authoritative source of risk and threat scoring data for IP addresses worldwide.
We analyze billions of daily global queries from a variety of industries including financial services, streaming media/OTT content distribution, advertising, insurance, gaming, government and healthcare. So, when a decision is made to identify an IP address as fraud risk or a security threat, you can be confident that you're using the most insightful IP Reputation data available.
With risk insight from UtraReputation data, you can determine if the IP is being used by an actual human being or is non-human bot or server traffic. You can determine if the IP address has been associated with malicious activity in the past and is too risky to trust, the history of the IP address and the last time it was seen, and if its risk profile has changed since the last time it was seen.
We assign a score between 1 and 5. To arrive at this score, we analyze usage patterns across key industries as well as collection of non-human activity (e.g. server and bot lists) to differentiate real end-user (human) traffic from non-human (server or bot) traffic. The higher the number (e.g. 5), the more likely the traffic is "non-human."
Score between 1 and 5
Output: Real-User Score
We assign a risk score between 1 and 100. This score provides insight about the risk of the IP address being associated with malicious activity from IP threat intelligence sources. The higher the risk score (e.g. 100), the more likely the IP has been associated with nefarious activity.
Score between 1 and 100
Output: Risk Score
As a leading provider of Marketing and Security services, Neustar is uniquely positioned to see network traffic patterns and threat actors across all verticals. By analyzing traffic behavior and identifying threats from the insight we collect from over 30 globally distributed nodes and over 100 billion DNS looks ups per day, we are able to identify query patterns that indicate human or non-human behavior and increased risk. We then use that data and insight to enhance the Real User Score. This proprietary view adds another layer of intelligence to help teams confidently determine if traffic is originating from a real-user (human), or if it is non-human (bot/server) traffic.