Machine Learning Vulnerability Analysis Using Variable Lifecycle Chains
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Solution Overview
Problem
Current security scans for software or code are not precise, often generating a high number of false positives, which requires significant time and effort for security engineers or developers to distinguish valid issues from false positives.
Innovation Solution
A system that utilizes machine learning models to analyze information from security scans, determining an initial risk factor score based on historical data, and refining it using code hierarchy and variable lifecycle chains to classify security issues as valid or false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security scans are performed to check for potential vulnerabilities, then security coverage is improved, but the number of false positives increases significantly
Solution Approach 1:
The system performs preliminary actions by analyzing code hierarchy and variable lifecycle chains before final vulnerability determination. Historical security scan data is processed in advance to train machine learning models, which then predict vulnerability likelihood before human review, reducing false positives while maintaining comprehensive security coverage.
Solution Approach 2:
The system implements feedback mechanisms where security engineers' manual assessments of false positives are fed back into the machine learning model to continuously improve its accuracy. The model learns from corrected predictions and updates its vulnerability detection algorithms, progressively reducing false positive rates while maintaining high security coverage.
2Measurement precision
If security engineers manually assess numerous security issues, then accuracy in distinguishing valid issues from false positives is improved, but the time required for analysis increases significantly
Solution Approach 1:
The system enables self-service by having the machine learning model automatically assess and prioritize security issues based on predicted vulnerability likelihood. The model autonomously analyzes code hierarchy and variable lifecycle chains, reducing the manual assessment burden on security engineers while maintaining high accuracy in distinguishing valid issues from false positives.
Solution Approach 2:
The system performs preliminary classification of security issues using machine learning models before human review is needed. By pre-assessing the likelihood of each issue being a true positive or false positive, the system enables security engineers to focus only on high-priority items, significantly reducing total analysis time while maintaining accuracy.
3Measurement precision
If detailed analysis of code hierarchy and logic is performed, then identification of valid security issues is improved, but the complexity of the analysis process increases
Solution Approach 1:
The system introduces machine learning models as intermediary components that bridge the gap between raw security scan data and human analyst interpretation. These models process complex code hierarchy and variable lifecycle chain information, translating it into actionable vulnerability predictions that reduce the perceived complexity while maintaining high identification accuracy.
Solution Approach 2:
The system segments the complex analysis process into distinct manageable components: security scan execution, code hierarchy analysis, variable lifecycle chain tracking, machine learning prediction, and human review. This segmentation reduces overall complexity by making each component independent and optimizable, while collectively achieving high precision in valid security issue identification.
Data Source
AI summary
According to a present invention embodiment, software is analyzed for security vulnerabilities. Information from a security scan of code is analyzed to identify a security issue pertaining to a code portion. A machine learning model determines an initial risk factor score for the security issue based on a history of information pertaining to one or more prior security scans. The initial risk factor score is refined to produce an overall risk factor score for the security issue based on a confidence of the initial risk factor score. The initial risk factor score is refined based on one or more risk factor scores for operations within the code applied to a variable of the code portion. A classification of the security issue is determined as one of a valid security issue and a false positive based on the overall risk factor score.