Automated Vulnerability Matching Confidence Scoring

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Solution Overview

Problem

The manual merging of pre-publication vulnerability information with published vulnerability information is a labor-intensive task for cybersecurity personnel, requiring efficient automation to prevent duplication and ensure coherent information delivery to customers.

Innovation Solution

A system that compares details of a security advisory against informal vulnerability records, generating a vulnerability match confidence value based on similarity comparisons. This value determines whether the security advisory can be automatically merged or requires manual examination, reducing the burden on cybersecurity personnel.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual merging of vulnerability information is performed, then accuracy of matching can be ensured, but labor intensity and time consumption increase significantly

Engineering Contradiction:

Improvematching accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent introduces a confidence score as an intermediary metric to bridge manual review and automated merging. The confidence score quantifies the likelihood of correct matching based on multiple data point comparisons, allowing the system to automatically merge high-confidence matches while routing low-confidence cases to manual review, thus reducing time consumption while maintaining accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the vulnerability merging process into distinct stages: automated confidence scoring based on multiple data point comparisons (vulnerability description, affected software, CVE ID, etc.), and conditional routing to either automated merging or manual review based on threshold criteria. This segmentation allows different processing approaches for different cases, optimizing both efficiency and accuracy

Inventive Principle:

Principle #1Segmentation

2Productivity

If automated merging is implemented, then productivity increases, but risk of incorrect merging and information loss increases

Engineering Contradiction:

Improvemerging efficiencyVSAvoidmerging accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements dynamic threshold adjustment where the confidence score thresholds for automated merging are not fixed but can be adjusted based on organizational risk tolerance, data quality, and operational context. This allows the system to adapt between more aggressive automated merging (higher productivity) and more conservative approaches (higher reliability) as needed

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent incorporates feedback mechanisms where manual review outcomes and merging results are used to refine and adjust confidence score calculations and thresholds over time. This continuous feedback loop improves the reliability of automated merging by learning from past performance while maintaining high productivity

Inventive Principle:

Principle #23Feedback

3Loss of information

If multiple data sources are monitored, then completeness of vulnerability information improves, but system complexity and processing burden increase

Engineering Contradiction:

Improveinformation completenessVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent creates a universal matching framework that can process and compare data from multiple different vulnerability information sources (internal vulnerability databases, external CVE feeds, security advisories, etc.) using a common set of comparison criteria and confidence scoring mechanisms. This multi-functional approach enables comprehensive information gathering while managing complexity through standardization

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12335290B2Automated matching of vulnerability data between vulnerability feeds

Publication Date: 2025.06.17 PALO ALTO NETWORKS INC

AI summary

A system has been designed that examines details of a security advisory against informal vulnerability records. The system generates a vulnerability match confidence value based on comparison of different details in the security advisory against the informal vulnerability records. Based on the comparisons, the system determines similarity of different details between the security advisory and the informal vulnerability records and cumulatively updates a vulnerability match confidence value with various detail similarity weights according to the determined similarities. Based on the vulnerability match confidence value, the system can classify or designate a security advisory for automatic merging or for manual examination. This reduces the burden on cybersecurity personnel and allows cybersecurity personnel to focus their limited resources on analyzing new vulnerabilities.