Automated Vulnerability Triage Using Machine Learning Models
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Solution Overview
Problem
Current methods for scanning and remedying security vulnerabilities in software applications during development are slow and manual, requiring expert interpretation and are hindered by a shortage of cybersecurity experts.
Innovation Solution
A system and method that includes a scan engine, vulnerability report engine, extraction engine, format engine, vector engine, classification engine, and output engine to automate the scanning, analysis, and remediation of security vulnerabilities in software applications, utilizing machine learning for automated triage and reducing false positives and duplicates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scanning and analysis methods are used, then expert interpretation quality is maintained, but the process speed and scalability deteriorate
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the vulnerability scanning process and expert analysts. These models automatically triage vulnerabilities, filter false positives, and prioritize findings, serving as a mediator that prepares data for expert review while maintaining interpretation quality and significantly improving process speed and scalability
Solution Approach 2:
The system implements automated self-service capabilities where machine learning algorithms automatically analyze scan results, classify vulnerabilities by severity, eliminate false positives, and generate prioritized reports without requiring constant expert intervention. This self-service approach handles routine analysis tasks while experts focus on complex cases
2Productivity
If more cybersecurity experts are hired to improve analysis capacity, then vulnerability assessment quality improves, but cost and resource requirements worsen
Solution Approach 1:
The patent creates virtual copies of expert analysis capabilities through machine learning models trained on historical expert assessments. These digital copies can simultaneously evaluate multiple vulnerabilities without fatigue or additional resource costs, effectively multiplying the analytical capacity available to the organization
Solution Approach 2:
The machine learning system serves multiple functions simultaneously: it acts as an initial scanner, filter for false positives, triage system, prioritization engine, and reporting generator. This multi-functional approach replaces what would otherwise require multiple specialized expert roles, reducing overall resource requirements while maintaining or improving productivity
3Productivity
If automated triage systems are implemented, then processing speed and scalability improve, but system complexity worsens
Solution Approach 1:
The patent divides the vulnerability analysis system into distinct modular components: initial scanning module, machine learning triage module, false positive filtering module, prioritization module, and expert review module. Each module handles a specific aspect of the analysis pipeline, making the overall complex system manageable through clear segmentation and independent optimization of each component
Data Source
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AI summary
Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.