Fraud Detection via Client Communication Fingerprinting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online enterprises face challenges in tracking and detecting fraudulent activities due to dynamic IP addresses and proxy servers, making it difficult to identify perpetrators, as they can access servers from various locations and appear as multiple users.
Innovation Solution
A system that extracts and analyzes identifying information from client communications, such as message headers, to create a 'fingerprint' for each user account, allowing for the identification of potential fraudulent activity by comparing future communications to previous ones, and alerting administrators of potential unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IP addresses are used to track user activity, then fraudulent activities can be detected, but dynamic IP assignment and proxy servers make perpetrator identification difficult
Solution Approach 1:
The patent extracts identifying information from multiple message headers (User-Agent, Accept-Language, Accept-Encoding, etc.) separately from the IP address. By taking out these identifying characteristics and analyzing them independently, the system can identify perpetrators even when IP addresses are dynamic or proxied, resolving the contradiction between tracking accuracy and system complexity
Solution Approach 2:
The patent segments the identification process into multiple independent components: extracting various message header fields, analyzing each field separately, and combining them to form a comprehensive perpetrator profile. This segmentation allows the system to overcome the limitations of relying on a single tracking mechanism like IP addresses
2Measurement precision
If multiple identifying characteristics are analyzed, then perpetrator identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent creates a universal analysis framework that handles multiple message header fields (User-Agent, Accept-Language, Accept-Encoding, Cache-Control, etc.) using the same extraction and comparison logic. This multi-functional approach improves detection accuracy while maintaining consistent processing complexity through a standardized analysis method
Solution Approach 2:
The system performs preliminary extraction and analysis of identifying information from message headers before fraudulent activities can be committed. By pre-establishing baseline identifying characteristics for each user account, the system can quickly compare future activities against these baselines, improving detection accuracy without increasing real-time processing complexity
3Reliability
If traditional IP tracking is used, then the system is simple to implement, but it cannot effectively track users behind proxy servers
Solution Approach 1:
The patent uses message header information as an intermediary to identify perpetrators when direct IP tracking fails. By analyzing characteristics in message headers that are harder to spoof than IP addresses, the system establishes a reliable tracking mechanism that works effectively even when users employ proxy servers
Solution Approach 2:
Instead of trying to identify users through their network location (IP address), the patent inverts the approach by identifying users through their client device characteristics (message headers). This inversion makes tracking more reliable because client device characteristics remain consistent even when network location changes due to dynamic IP assignment or proxy usage
Data Source
AI summary
A system for identifying perpetrators of fraudulent activity includes location logic for locating, extracting, or capturing identifying information from a client communication received from a client device. For example, the location logic may locate, or extract, a variety of message headers from an HTTP client request. The system may also include analyzer logic to analyze the identifying information, for example, by comparing the identifying information with previously captured identifying information from a previously received client communication. Finally, the system may include account identifier logic to identify user accounts associated with the previous client communication in which the same identifying information was extracted.