Vulnerability Prioritization via Machine Learning Exploitability

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Solution Overview

Problem

Existing cybersecurity systems lack an effective method to prioritize and remediate security vulnerabilities based on organization-specific risk levels, as generic scoring systems like CVSS do not consider changing vulnerability urgency and organizational-specific factors.

Innovation Solution

A method and system using a machine-learning algorithm to determine exploitability levels of security vulnerabilities by combining internal and external data, and then prioritizing remediation procedures based on these levels and organization-specific criteria, with a vulnerability manager transmitting remediation commands to network elements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If generic scoring systems like CVSS are used to prioritize vulnerabilities, then the system is simple and easy to operate, but it does not consider organization-specific risk levels and changing vulnerability urgency

Engineering Contradiction:

Improveorganization-specific vulnerability prioritizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The vulnerability prioritization system is segmented into multiple independent components: a machine learning model for exploitability prediction, an organization-specific criteria module for risk assessment, and a remediation prioritization engine. This segmentation allows each component to be developed and maintained independently while working together to provide tailored vulnerability prioritization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A machine learning model serves as an intermediary between raw vulnerability data and prioritization decisions. The model processes exploitability predictions and transforms them into actionable prioritization scores that reflect both technical vulnerability characteristics and organization-specific risk factors.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine-learning algorithms are used to determine exploitability levels, then vulnerability prioritization accuracy is improved, but computational resources and processing time increase

Engineering Contradiction:

Improveexploitability level accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The machine learning model is trained in advance on historical vulnerability data and exploitability patterns. This preliminary training enables the model to make rapid exploitability predictions during actual vulnerability assessments without requiring real-time complex computations, thus reducing processing time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system focuses computational resources on predicting exploitability levels for the most critical vulnerabilities first, rather than uniformly processing all vulnerabilities. This partial action approach ensures high accuracy for high-impact vulnerabilities while reducing overall processing time.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If comprehensive internal and external data are combined for vulnerability assessment, then the prioritization accuracy is improved, but data processing complexity and storage requirements increase

Engineering Contradiction:

Improvevulnerability assessment accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Vulnerability data is segmented into distinct categories: internal organizational data (asset criticality, security controls) and external data (CVE databases, exploit availability, threat intelligence). Each data segment is processed independently by specialized modules before being integrated into the final prioritization score.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs a universal data processing framework that can handle multiple data types and sources through standardized interfaces. This multi-functional architecture allows the same processing pipeline to accommodate various internal and external data formats without requiring separate processing systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11477231B2System and method for vulnerability remediation prioritization

Publication Date: 2022.10.18 SAUDI ARABIAN OIL CO

AI summary

A method may include obtaining internal vulnerability data and external vulnerability data regarding various security vulnerabilities among various network elements for a predetermined organization. The method may include determining various exploitability levels for the security vulnerabilities using a model, the external vulnerability data, and the internal vulnerability data. The model may be generated using a machine-learning algorithm. The method may include determining a vulnerability priority for the plurality of security vulnerabilities using the plurality of exploitability levels and organization-specific criteria. The vulnerability priority may describe a sequence that the security vulnerabilities are remediated. The method may further include transmitting a remediation command to one of the network elements. The remediation command may initiate a remediation procedure at the network element to address the security vulnerability.