Click Fraud Detection via URL Trend Analysis
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Solution Overview
Problem
Current methods for detecting click fraud in pay per click (PPC) advertising are inadequate, as they often rely on pre-defined campaign detection and can be circumvented by sophisticated scammers, leading to undetected fraudulent clicks and revenue loss for advertisers.
Innovation Solution
A system that analyzes URL activity in relation to historical and global trends to distinguish between normal and suspicious behavior, generating click fraud reports and disabling access to referring resources for suspected offenders, using data gathered from search engine use patterns to identify and prevent fraudulent clicks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional click fraud detection methods are used, then detection simplicity is maintained, but detection precision deteriorates due to sophisticated scammers circumventing pre-defined campaign detection
Solution Approach 1:
The patent segments the detection approach by analyzing individual URL activity patterns separately and comparing them against historical and global trends. This allows detection of suspicious patterns without requiring complex pre-defined campaign rules, thereby improving detection precision while managing system complexity through modular analysis of discrete URL behaviors
Solution Approach 2:
The patent introduces a new dimension of analysis by comparing URL activity against historical trends and global activity patterns across multiple URLs. This multi-dimensional comparison approach enables detection of fraudulent clicks that circumvent traditional single-campaign detection methods, improving precision without proportionally increasing complexity
2Measurement precision
If sophisticated detection systems are implemented to identify fraudulent clicks, then detection precision improves, but device complexity increases making the system harder to operate
Solution Approach 1:
The system automatically compares URL activity patterns against historical and global trends without requiring manual configuration or intervention. The automated analysis of activity data against established baselines enables high detection precision while maintaining ease of operation, as the system self-adjusts and operates without complex user input
Solution Approach 2:
The system continuously monitors URL activity and compares it against historical patterns, providing automatic feedback on suspicious patterns. This feedback mechanism enables accurate fraud detection while simplifying operation, as the system automatically adjusts its detection based on ongoing analysis without requiring manual tuning or complex user interaction
Data Source
AI summary
Systems and methods for detecting instances of click fraud are disclosed. Click fraud occurs when, for example, a user, malware, bot, or the like, clicks on a pay per click advertisement (e.g., hyperlink), a paid search listing, or the like without a good faith interest in the underlying subject of the hyperlink. Such fraudulent clicks can be expensive for an advertising sponsor. Statistical information, such as ratios of unpaid clicks to pay per clicks, are extracted from an event database. The statistical information of global data is used as a reference data set to compare to similar statistical information for a local data set under analysis. In one embodiment, when the statistical data sets match relatively well, no click fraud is determined to have occurred, and when the statistical data sets do not match relatively well, click fraud is determined to have occurred.