ML Vulnerability Refinement for False Positive Reduction
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Solution Overview
Problem
Existing vulnerability detection systems face challenges in managing large numbers of computing systems, leading to difficulties in maintaining each system and managing their respective vulnerabilities, due to the proliferation of software code and attendant vulnerabilities, and the ambiguity in vulnerability reports generated by different scanning tools.
Innovation Solution
An improved machine learning (ML)-based technique that refines and enhances the detection and management of system-wide vulnerabilities by comparing and intelligently updating a vulnerability database. This involves a ML training feed methodology that categorizes raw vulnerabilities into data sets for training, using a neural network-based architecture to derive and classify vulnerability characteristics, and generating refined vulnerability parameters to improve the accuracy of vulnerability reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple vulnerability scanning tools are used to detect vulnerabilities across numerous computing systems, then the coverage and detection capability are improved, but the ambiguity and false positive findings in vulnerability reports increase
Solution Approach 1:
The patent segments vulnerability data processing into distinct phases: initial scanning by multiple tools, centralized data aggregation, ML-based analysis, and refined reporting. This segmentation allows each component to specialize in specific tasks, improving overall detection accuracy while reducing report ambiguity through systematic processing stages.
Solution Approach 2:
The patent introduces an intermediary ML-based vulnerability analysis system that sits between multiple scanning tools and the final reporting process. This intermediary aggregates data from various sources, applies intelligent analysis to resolve conflicts and false positives, and produces unified, accurate vulnerability reports, thereby reducing report ambiguity while maintaining detection coverage.
2Reliability
If traditional vulnerability management processes are used to manage a large number of computing systems, then comprehensive vulnerability coverage is achieved, but the resource requirements and management complexity increase substantially
Solution Approach 1:
The patent replaces manual, mechanical vulnerability management processes with an automated ML-based system. The system automatically aggregates vulnerability data from multiple sources, performs intelligent analysis, prioritizes findings, and generates reports without substantial human intervention, thereby reducing management complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent changes the operational parameters of vulnerability management by introducing ML-based analysis that dynamically adjusts prioritization criteria, false positive thresholds, and resource allocation based on learned patterns from historical data. This allows the system to maintain comprehensive coverage while optimizing resource usage and reducing complexity through adaptive parameter adjustment.
3Measurement precision
If comprehensive vulnerability scanning is performed across all systems, then complete vulnerability detection is achieved, but the time and resources required for processing and managing findings increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing vulnerability data through centralized aggregation and ML-based analysis before final reporting. The system pre-identifies false positives, pre-prioritizes findings, and pre-validates vulnerability data from multiple sources, thereby reducing the time required for subsequent processing while maintaining complete detection coverage.
Solution Approach 2:
The patent implements continuous vulnerability monitoring and analysis, where the ML system continuously processes vulnerability data from multiple scanning tools as they are generated. This continuous processing eliminates batch processing delays, maintains complete detection coverage through ongoing analysis, and reduces overall processing time by keeping the system in a constant state of useful action.
Data Source
AI summary
A system and method of processing data on detected vulnerabilities using a learning vulnerability processing model to generate refined vulnerability data that excludes one or more of a false positive finding, a repeated item, and an inaccurate finding assignment, the learning vulnerability processing model being trained and evaluated using a task component that outputs one or more evaluation processes for a corresponding one or more processed vulnerability records and a performance measurement component that executes the one or more evaluation processes to output one or more evaluation metrics, the one or more evaluation metrics comprising a comparison metric for a comparison between respective one or more potential error indicators in the raw vulnerability data and corresponding one or more vulnerability type classifications using the learning vulnerability processing model.