3 Core Data Components of Accurate IP Fraud Scores

Free IP lookup API to uncover fraud, bots, and high risk users.

For IP fraud scoring, the database is one of the most important factors for its accuracy. If the data used is subpar, then the fraud score will be similarly flawed. The best IP databases optimize three core components to make their data more actionable and accurate:

  • Database size
  • Data collection
  • Data refresh rates

If any one of these components is not done correctly, fraud scores won’t be as accurate as they could be.

Database Size

IP fraud checks rely on extensive databases, which can be categorized into two main types: publicly available and proprietary.

Publicly available databases include block lists like DNSBL (Domain Name System Blackhole List) and RBL (Real-time Blackhole List), which catalog IP addresses known for malicious activities. IP fraud scores use this publicly available data in conjunction with their own.

Proprietary databases, on the other hand, are developed and maintained by fraud detection companies. These databases are often more comprehensive and up to date, incorporating data from various sources and using advanced algorithms to analyze IP behavior patterns

The size and quality of these databases directly impact the accuracy of IP fraud scores. Larger, more comprehensive databases offer several advantages:

  1. More historical context: A longer history of IP address behavior allows for a more accurate risk assessment based on past activities.
  2. Broader coverage: More extensive databases are likely to have information on a wider range of IP addresses, reducing the chances of encountering unknown IPs and inaccurate assessments.
  3. Better pattern recognition: With more data points, these systems can better identify complex fraud patterns and emerging threats.
  4. Real-time updates: Larger databases often benefit from more frequent updates, ensuring that the latest fraud trends are quickly incorporated into the scoring system.

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IP Data Collection Practices

Every day, benign IP addresses will suddenly become sources of systemic fraud. So the accuracy of an IP fraud score hinges on a platform’s ability to actively collect and integrate new IP data. There are four ways new IP fraud data is collected:

  • Honeypot Traps: This method involves placing code snippets on websites that are invisible to legitimate users but act as decoys to attract and trap malicious actors. These systems are intentionally designed with apparent vulnerabilities, mimicking various targets like databases or payment gateways. When these traps are triggered, the associated IP addresses are immediately flagged as high-risk in the database, providing early detection of potential threats.
  • Device Monitoring: Data on the number and variety of devices that use IP addresses implies higher risk if it exceeds the norms. Analysis of the devices associated with IP addresses offers greater context for risk evaluation. This method can reveal patterns such as device spoofing or the use of emulators, which are often indicators of fraudulent intent.

The effectiveness of an IP risk scoring system is directly proportional to the diversity and sophistication of its data collection methods. Platforms that employ a multi-faceted approach to gathering IP reputation data are better equipped to provide accurate, real-time risk assessments, crucial for maintaining robust cybersecurity defenses in today’s dynamic digital landscape.

Data Refresh Rates

Malicious actors move fast. They constantly change their tactics, rotating through IP addresses or exploiting newly compromised systems. This dynamic nature of cyber threats necessitates real-time or near-real-time updates to IP reputation databases. An IP fraud database should be automatically refreshed at least every hour, ideally more frequently.

The effectiveness of IP risk assessment heavily relies on the freshness of the underlying database. A database that is hours old can be outdated, missing crucial information about emerging risks.

Fraudlogix’s databases are updated hourly with data from the largest sensor network in the world. This is fed into the database to keep blocklists and IP fraud data as up to date as possible.