Software Vulnerability Exploitation Prediction for Patch Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for prioritizing software vulnerability patching are inefficient, leading to a high false alarm rate and failing to accurately predict which vulnerabilities will be exploited, with existing systems like CVSS and social media-based approaches being inadequate.
Innovation Solution
A machine learning model utilizing cyber threat intelligence feeds from ExploitDB, Zero Day Initiative, and darkweb/deepweb data sources, combined with features like TF-IDF, CVSS scores, and social network analysis, to predict vulnerability exploitation with high precision and low false positive rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard risk assessment systems (CVSS, Microsoft Exploitability Index, Adobe Priority Rating) are used to prioritize vulnerabilities, then the organization takes a cautious approach by flagging many vulnerabilities as severe, but the false alarm rate increases significantly since the majority of flagged vulnerabilities will not be attacked
Solution Approach 1:
The patent transforms the vulnerability assessment from static scoring (CVSS) to dynamic prediction by introducing temporal parameters such as time since disclosure, presence of exploits in wild, and exploitation likelihood scores. This allows the system to adapt predictions based on changing threat landscape while reducing false alarms through probabilistic filtering
Solution Approach 2:
The patent introduces an intermediary prediction layer between vulnerability disclosure and patching decisions. This intermediary system uses multiple data sources (NVD, ExploitDB, darkweb feeds) and machine learning models to filter and prioritize vulnerabilities, acting as a mediator that reduces the false alarm rate while maintaining reliable identification of exploitable vulnerabilities
2Reliability
If organizations patch all vulnerabilities flagged as severe by standard systems, then security coverage is improved, but resource efficiency decreases due to the high volume of false alarms
Solution Approach 1:
The patent applies partial action by focusing patching resources only on vulnerabilities with high exploitation likelihood scores rather than all severe vulnerabilities. The system identifies and prioritizes the critical subset of vulnerabilities that pose actual exploitation risk, allowing organizations to apply patches selectively to maintain security coverage while improving resource efficiency
Solution Approach 2:
The patent performs preliminary prediction and filtering of vulnerabilities before the patching decision is made. By pre-calculating exploitation likelihood scores and prioritizing vulnerabilities based on predicted exploitation risk, the system enables organizations to prepare patching resources in advance for the most critical vulnerabilities, improving both security coverage and resource efficiency
3Measurement precision
If more data sources are integrated into the prediction model, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex prediction system into modular components: data collection modules (NVD, ExploitDB, darkweb feeds), feature extraction modules (TF-IDF, social network analysis), prediction modules (machine learning models), and output modules (prioritization scores). This segmentation allows each component to be developed and maintained independently while improving overall prediction accuracy through integrated multi-source data
Data Source
AI summary
Various embodiments for predicting which software vulnerabilities will be exploited by malicious hackers and hence prioritized by patching are disclosed.