Click Engine Fraud Classification
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Solution Overview
Problem
Click fraud, which involves fraudulent click-throughs on Internet advertisements, costs advertisers significantly, with estimates suggesting it constitutes up to 20% of all Internet traffic, affecting the advertising budget and revenue distribution.
Innovation Solution
A click engine is introduced within a communications network to redirect and analyze HTTP flows, checking for signatures indicative of fraudulent behavior and classifying them based on quality, allowing for appropriate actions to be taken at the service provider or advertiser level, such as modifying packet flows with classification information for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If click fraud is allowed to occur freely, then advertising revenue can be generated, but advertisers lose money due to fraudulent clicks
Solution Approach 1:
A click engine is introduced as an intermediary component between the advertisement delivery system and the billing system. This click engine intercepts click-through events, analyzes them for fraud indicators, and determines whether to allow the click to be billed. The intermediary filters out fraudulent clicks while permitting legitimate ones, thus protecting advertising budgets without completely blocking revenue generation.
Solution Approach 2:
The system performs preliminary analysis of click-through events before billing occurs. The click engine examines various attributes of the click (source IP, user behavior patterns, timing, etc.) in advance to identify potential fraud. By performing this analysis beforehand, the system can prevent fraudulent billing while allowing legitimate clicks to proceed to normal billing processes.
2Measurement precision
If all click-throughs are analyzed in detail, then fraud detection accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The click analysis process is segmented into multiple independent components within the click engine. Different analysis functions (IP validation, behavior pattern recognition, timing analysis) are separated into distinct modules. This segmentation allows the system to apply detailed analysis only where needed while keeping other parts simple, thereby improving detection accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The system applies different levels of analysis quality to different click scenarios. High-quality detailed analysis is applied to suspicious or high-value clicks, while routine legitimate clicks receive simpler validation. This local differentiation of quality levels improves fraud detection accuracy for critical cases while maintaining system efficiency and managing complexity.
Data Source
AI summary
In one embodiment, a method includes receiving a packet flow associated with a click-through from an end user node destined for an advertiser server; extracting information from the packet flow; analyzing the extracted information to determine one or more characteristics of the packet flow; and classifying the packet flow based on the determined one or more characteristics; modifying the packet flow to include classification information to provide classification information indicating a quality level of the click-through. The packet flow may include a hypertext transfer protocol GET request. Modifying the packet flow may include adding a tag with classification information that indicates a likelihood of fraudulent click behavior associated with the packet flow.