Machine Learning Model for Opinionated Cybersecurity Vulnerability Assessments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity threat assessments for vulnerabilities rely heavily on human expertise, which are slow and inadequate due to the labor-intensive nature of collecting and analyzing disparate data sources, making it difficult to scale with the growing number of un-assessed security vulnerabilities.
Innovation Solution
A machine learning-based system that combines intrinsic attributes from the Common Vulnerability Scoring System (CVSS) with subjective attributes from AttackerKB, using a regression model to generate opinionated threat assessments for security vulnerabilities, allowing for automated generation of attacker value and exploitability values without requiring new subjective inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used to perform threat assessments of security vulnerabilities, then the quality and accuracy of assessments are improved, but the time required and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and organizing vulnerability data, intrinsic attributes, and historical assessment information before actual threat assessment is needed. This preprocessing enables the machine learning model to generate rapid assessments without requiring real-time human intervention, thus reducing assessment time while maintaining quality through pre-trained models
Solution Approach 2:
A machine learning model serves as an intermediary between raw vulnerability data and final threat assessments. The model is trained on historical human assessments and intrinsic attributes, then acts as a mediator to generate new assessments automatically, bridging the gap between data and expert-level analysis without requiring continuous human involvement
2Reliability
If human experts manually assess security vulnerabilities, then opinionated threat assessments with subjective attributes are obtained, but the scalability is limited due to labor-intensive processes
Solution Approach 1:
The system creates copies of human expert knowledge by training machine learning models on historical human assessments. The model learns to replicate human experts' reasoning patterns and judgment criteria, enabling it to generate reliable assessments that mirror human expert output at scale without requiring actual human experts for each assessment
Solution Approach 2:
The system transforms subjective human assessments into objective parameters by extracting intrinsic attributes (attack vector, complexity, user interaction) and using them as input features for the machine learning model. This parameterization enables automated processing while preserving the essence of human expert judgment through learned relationships between parameters
3Measurement precision
If comprehensive data collection from multiple sources is performed for vulnerability assessment, then the accuracy of threat assessments is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the vulnerability assessment process into distinct components: data collection from multiple sources, extraction of intrinsic attributes, model training phase, and inference phase. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex task of comprehensive assessment into manageable, modular stages
Data Source
AI summary
Disclosed herein are methods, systems, processes, and machine learned models for performing opinionated threat assessments for cybersecurity vulnerabilities. An opinionated threat assessment system is implemented that obtains a training dataset that includes a codified opinionated threat assessment for security vulnerabilities. The codified opinionated threat assessment in the training dataset includes intrinsic attributes for the security vulnerabilities and subject attributes about the security vulnerabilities. The opinionated threat assessment system trains an opinionated threat assessment model using the training dataset and according to a machine learning technique where the training tunes the opinionated threat assessment model to generate a machined learned opinionated threat assessment for a new security vulnerability based on new intrinsic attributes associated with the new security vulnerability.