Fraud Detection via IP and Registration Analysis
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Solution Overview
Problem
Current methods for detecting online fraud, such as phishing and identity theft, are inadequate in proactively identifying fraudulent webpages, as fraudsters often change website addresses, making blacklisting ineffective.
Innovation Solution
A computer system and method that identifies known fraudulent Internet domains by analyzing candidate domains based on IP addresses and domain name registration data, determining if they satisfy registration conditions, and then analyzing electronic documents for fraudulence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blacklisting known fraudulent domains is used to detect online fraud, then fraudulent websites can be identified, but fraudsters frequently change website addresses making the detection ineffective
Solution Approach 1:
The system performs preliminary actions by analyzing domain registration data and electronic documents before the domains are actively used for fraud. It proactively identifies potential fraudulent domains by examining registration conditions and content characteristics, blocking them before they can be exploited, thus maintaining detection effectiveness despite domain address changes
Solution Approach 2:
The system introduces intermediary analysis layers between domain identification and fraud detection. Instead of directly matching domain addresses, it uses domain registration data and electronic document analysis as intermediaries to infer fraudulent intent, enabling detection of fraudsters who frequently change their website addresses
2Productivity
If domain name registration data is analyzed to identify fraudulent domains, then proactive detection is enabled, but the system complexity increases
Solution Approach 1:
The system segments the fraud detection process into distinct analytical modules: one for analyzing domain registration data, another for examining electronic documents, and a third for making fraud determination decisions. This segmentation allows each module to specialize in specific data types and detection methods, managing system complexity while enabling comprehensive proactive detection
Solution Approach 2:
The system performs preliminary analysis of domain registration data and electronic documents before final fraud determination. By conducting these analyses in advance and systematically, the system establishes a structured workflow that manages complexity while enabling proactive detection of fraudulent domains before they are actively exploited
Data Source
AI summary
Described systems and methods enable a swift and efficient detection of fraudulent Internet domains, i.e., domains used to host or distribute fraudulent electronic documents such as fraudulent webpages and electronic messages. Some embodiments use a reverse IP analysis to select a set of fraud candidates from among a set of domains hosted at the same IP address as a known fraudulent domain. The candidate set is further filtered according to domain registration data. Online content hosted at each filtered candidate domain is further analyzed to identify truly fraudulent domains. A security module may then prevent users from accessing a content of such domains.