Fraudulent Internet Traffic Detection via Intermediate Validation
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Solution Overview
Problem
In Pay Per Click advertising systems, advertisers face challenges in differentiating between genuine and fraudulent internet traffic, leading to unfair compensation for affiliates who use automated or incentivized clicks to maximize revenue.
Innovation Solution
A method to detect and monitor bad internet traffic by validating traffic sources using data from advertisement clicks, survey forms, and a database of known fraudulent sites, which includes redirecting users through an intermediate page to gather information and initiate validation requests, and analyzing this data to identify and block fraudulent affiliates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If affiliates use automated or incentivized clicks to maximize referrals, then affiliate revenue increases, but advertiser compensation becomes unfair and revenue is lost
Solution Approach 1:
The system performs preliminary validation of referral traffic by analyzing multiple data points (browser information, survey responses, behavioral patterns) before compensating the affiliate. This preliminary action identifies fraudulent traffic sources in advance, preventing unfair compensation while allowing legitimate referrals to proceed normally.
Solution Approach 2:
The system implements a feedback mechanism where referral traffic is monitored and analyzed after the click but before compensation is finalized. By providing feedback on traffic quality through validation requests and data analysis, the system can distinguish between genuine and fraudulent referrals, ensuring advertisers only compensate for legitimate traffic.
2Productivity
If advertisers compensate for all clicks regardless of authenticity, then affiliate incentive is maximized, but advertiser revenue is compromised
Solution Approach 1:
The system introduces an intermediary validation layer between the affiliate referral and the advertiser compensation. This intermediary analyzes browser information, survey responses, and behavioral data to determine referral authenticity. Legitimate referrals pass through smoothly maintaining affiliate incentive, while fraudulent ones are identified and blocked, preventing revenue loss.
Solution Approach 2:
The system changes the parameters used to determine compensation eligibility from a simple click count to a multi-parameter validation system. By analyzing browser version, language settings, survey responses, and behavioral patterns, the system creates a more nuanced compensation criterion that maintains incentive for genuine referrals while filtering out fraudulent traffic.
3Reliability
If validation requests are implemented to detect fraudulent traffic, then compensation integrity improves, but system complexity increases
Solution Approach 1:
The validation system is segmented into distinct functional modules: browser information collection, survey form delivery and response analysis, behavioral pattern monitoring, and decision-making logic. Each module handles a specific aspect of validation, making the overall complex system manageable and maintainable while ensuring comprehensive fraud detection.
4Measurement precision
If multiple data points are collected for validation, then fraud detection accuracy improves, but data processing requirements increase
Solution Approach 1:
The system applies partial validation actions based on risk assessment. Not all referrals undergo the complete validation sequence with multiple survey questions and extensive data collection. The system collects browser information and behavioral data for all referrals, but only subjects suspicious or high-risk traffic to more intensive validation, reducing overall processing energy while maintaining high detection accuracy for fraudulent traffic.
Data Source
AI summary
A method of detecting fraudulent Internet traffic sent from a first web site to a second web site including providing a first web site database having a list of first web sites likely to send bad traffic, providing a link to the second web site on the first web site, after an Internet user having a web browser clicks on the link, transferring the Internet user to an intermediate web site that gathers information from the Internet user web browser; and determining if a validation request is required.