Software Vulnerability Exploitability Prediction and Alternative Package Ranking
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Solution Overview
Problem
Current solutions only identify software vulnerabilities after installation, leaving systems prone to exploitation during the window of exploitability, which can lead to data breaches and security flaws.
Innovation Solution
A computer-implemented method using machine learning and natural language generation to evaluate the exploitability of software vulnerabilities before installation, identifying and ranking alternative software packages based on their exploitability scores, and providing insights to users for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vulnerability assessment is performed after installation using traditional methods, then the assessment process is simple and straightforward, but the system remains exposed to exploitation during the window of exploitability
Solution Approach 1:
The patent performs vulnerability assessment before software installation by analyzing package manifests, dependency graphs, and vulnerability databases in advance. This preliminary action identifies and evaluates vulnerabilities prior to deployment, eliminating the exposure window that exists when assessment occurs after installation. The system pre-calculates exploitability scores and generates remediation recommendations before the software is ever introduced to the production environment.
2Measurement precision
If comprehensive vulnerability data from multiple sources is collected and analyzed, then the accuracy of exploitability prediction improves, but the complexity of the assessment system increases
Solution Approach 1:
The patent introduces an intermediary processing layer that aggregates vulnerability data from multiple sources (NVD, CVE databases, security research feeds) and transforms it into structured exploitability scores. This intermediary layer uses standardized schemas and normalization processes to handle diverse data formats, making the complex multi-source data integration manageable while maintaining high prediction accuracy through comprehensive analysis.
Solution Approach 2:
The system transforms raw vulnerability data into standardized parameters including exploitability scores, risk ratings, and priority levels. By changing the parameter representation from unstructured text descriptions to quantified metrics, the system maintains measurement precision while reducing the operational complexity of analyzing and comparing vulnerability information across multiple sources.
3Measurement precision
If alternative software packages are identified and ranked based on comparative analysis, then the recommendation quality improves, but the computational resources and time required increase
Solution Approach 1:
The patent pre-generates and caches vulnerability profiles, exploitability scores, and compatibility information for software packages during off-peak periods or as part of package repository updates. This preliminary preparation allows the system to quickly retrieve and compare pre-analyzed data when users seek recommendations, maintaining high recommendation quality while significantly reducing the computational time and resources required during actual assessment operations.
Data Source
AI summary
Identifying and evaluating exploitability of software vulnerabilities is provided. A vulnerability identified and a level of exploitability of the vulnerability corresponding to a software package is evaluated prior to installation of the software package on a data processing system based on data collected from a plurality of software vulnerability data sources. Related alternative software packages corresponding to the software package to be installed on the data processing system are identified based on a comparative analysis between alternative software packages and the software package. A confidence level is determined for each respective related alternative software package for resolving the level of exploitability. The related alternative software packages are ranked from least to most vulnerable based on a calculated exploitability score corresponding to each respective related alternative software package. Insights are generated based on determined confidence levels and rankings corresponding to calculated exploitability scores of the related alternative software packages.