Co-visitation Factor Analysis for Non-Intended Traffic Detection
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Solution Overview
Problem
Existing methods for detecting non-intended network traffic are inadequate as they require individual identification of mechanisms used by each network location, making it difficult to distinguish between intended and non-intended user visits.
Innovation Solution
A system that calculates a co-visitation factor for website locations based on shared user access data, flagging locations with co-visitation factors above a predefined threshold as suspicious, indicating potential non-intended traffic, using a processor-readable medium with instructions to receive and analyze access data and set flags for target website locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual identification of mechanisms is performed for each network location, then detection accuracy for non-intended traffic is improved, but device complexity and processing time increase significantly
Solution Approach 1:
The patent merges the detection of multiple mechanisms into a unified approach by analyzing co-visitation patterns across network locations. Instead of individually identifying mechanisms for each location, the system combines visitation data from multiple sources to detect shared patterns that indicate non-intended traffic, thereby reducing system complexity while maintaining detection accuracy.
Solution Approach 2:
The system creates a universal detection mechanism that applies the same co-visitation analysis approach across all network locations. This multi-functional approach allows a single analytical framework to detect non-intended traffic patterns regardless of the specific network location or mechanism used, eliminating the need for location-specific individual identification methods.
2Productivity
If co-visitation factor threshold is set low, then more website locations are flagged as suspicious, but false positive rate increases
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor and adjust the co-visitation factor thresholds based on observed traffic patterns. By analyzing the distribution of co-visitation factors across network locations and comparing them against baseline data, the system dynamically optimizes threshold settings to maximize detection coverage while minimizing false positives, ensuring reliable identification of actual non-intended traffic.
Data Source
AI summary
A non-transitory processor-readable medium is provided that stores code representing instructions to be executed by a processor to receive data associated with access by a first plurality of entities to a first website location and to receive data associated with access by a second plurality of entities to a second website location. The processor is also caused to define a co-visitation factor for each of the first website location and the second website location based on the received data. The processor is also caused to, if the co-visitation factor of the first website location and/or the co-visitation factor of the second website location is over a predefined threshold, select the first website location and/or the second website location as target website locations. The processor is caused to send a signal to set a flag associated with each target website location indicating the target website location as a suspicious website location.