Incident Review Queue for False Positive Reduction
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Solution Overview
Problem
Conventional approaches to detecting and preventing illegitimate incidents in online services are time-consuming, require significant manual resources, and often result in misidentification of legitimate incidents as illegitimate, leading to false positives and reduced user experience.
Innovation Solution
A system and method that identifies potentially misidentified illegitimate incidents by applying rules based on characteristics such as source, domain, social connections, messaging, bid types, and landing pages to determine a threshold likelihood of misidentification, thereby preventing automatic disabling and placing such incidents in a queue for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic detection and disabling of illegitimate incidents is implemented, then productivity and security are improved, but false positives increase and user experience deteriorates
Solution Approach 1:
The system segments the automatic disabling process by introducing a review queue that separates high-risk incidents (which are automatically disabled) from low-risk incidents (which are flagged for manual review). This segmentation allows the system to maintain high automation for clear-cut cases while applying human judgment to edge cases, thereby improving both productivity and reliability simultaneously.
Solution Approach 2:
The review queue acts as an intermediary mechanism between automatic detection and final disabling actions. Incidents that fall into the gray area are held in this intermediate state, allowing manual verification before permanent disabling. This intermediary layer reduces false positives while maintaining the efficiency of automatic detection for definitive cases.
2Reliability
If manual review of all illegitimate incidents is performed, then identification accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
Instead of manually reviewing all incidents, the system applies partial manual review only to incidents that meet specific criteria (those flagged as potentially misidentified). The majority of clear-cut illegitimate incidents are handled automatically, while only the ambiguous cases require manual attention. This partial action approach maintains high accuracy where needed while minimizing time loss.
Solution Approach 2:
The system applies different processing qualities to different incidents: high-speed automatic processing for clear-cut cases and thorough manual review for ambiguous cases. This local differentiation of processing quality ensures that resources are concentrated where they are most needed (on difficult cases) while maintaining efficiency on straightforward cases.
3Productivity
If automated rules are applied to identify illegitimate incidents, then productivity is improved, but device complexity increases
Solution Approach 1:
The rule-based system is segmented into multiple independent rule modules, each evaluating specific characteristics (source reputation, domain validity, social connection patterns, messaging behavior, bid type anomalies, landing page quality). This modular segmentation makes the complex system more manageable, maintainable, and adaptable while preserving high processing throughput.
Solution Approach 2:
The rule engine is designed as a universal framework that can evaluate multiple different incident types using the same underlying architecture. The system multi-functionally assesses various characteristics (technical, social, behavioral) through a single unified rule evaluation mechanism, reducing overall system complexity while maintaining comprehensive detection capability.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can access a set of identified illegitimate incidents intended to be automatically disabled. One or more rules for determining misidentified illegitimate incidents can be acquired. It can be determined, based on the one or more rules, that at least a subset, out of the set of identified illegitimate incidents, has at least a threshold likelihood of being misidentified. Further processing can be performed with respect to at least the subset to prevent at least the subset from being automatically disabled.