Multi-stage ML Model Training for Vulnerability Exploit Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for remediating software vulnerabilities are inefficient as they rely on waiting for exploits to be developed and used, exposing computing assets to significant risk, and there is a need for predicting whether an exploit will be developed and used for a particular vulnerability.
Innovation Solution
A multi-stage machine learning approach is employed to generate predictive models that forecast the likelihood of exploit development and usage, utilizing training data to identify vulnerabilities at risk and adjust risk scores accordingly, allowing for prioritization of remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vulnerabilities are prioritized for remediation based on waiting for exploits to be developed and attacks to occur, then remediation resources are allocated according to actual threat levels, but computing assets are exposed to significant risk during the waiting period
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict which vulnerabilities are likely to have exploits developed for them before exploits actually exist. The system analyzes vulnerability characteristics, historical data, and other features to generate predictions about future exploit development, allowing organizations to remediate vulnerabilities proactively based on predicted risk rather than waiting for actual exploits or attacks to occur.
2Measurement precision
If machine learning models are trained on limited training data, then training resources are conserved, but prediction accuracy decreases
Solution Approach 1:
The patent applies segmentation by dividing the training process into multiple stages. The first stage trains an initial model on available training data to generate predictions. The second stage uses these predictions as additional training features to train a second model on the same original training data. This segmented approach allows the system to achieve higher prediction accuracy by effectively utilizing the same limited training data through multiple processing passes, rather than requiring proportionally more raw training data.
Solution Approach 2:
The patent uses an intermediary approach by introducing prediction outputs from the first machine learning model as intermediate training features for the second model. These intermediate predictions serve as a bridge that transforms the relationship between original training data and final prediction accuracy, allowing the second model to learn from both original features and predicted risk assessments, thereby improving overall accuracy without requiring additional raw training data.
3Measurement precision
If multiple machine learning models are trained in sequence, then prediction accuracy improves, but computational complexity and training time increase
Solution Approach 1:
The patent segments the complex task of high-accuracy vulnerability prediction into two manageable stages. The first stage trains a model on original training data to generate initial predictions. The second stage trains another model on the same original data plus the first model's predictions. This segmentation breaks down the complexity of training a single highly complex model into two simpler sequential training processes, making the overall system more manageable while achieving superior accuracy.
Data Source
AI summary
Generation of a first prediction model is caused based on first training data, where the first prediction model enables determining whether an exploit to be developed for software vulnerabilities will be used in an attack. For each training instance in the first training data, the first prediction model is used to generate a score. Each training instance is added to second training data if the score is greater than a threshold value. The second training data is a subset of the first training data. Generation of a second prediction model is caused based on the second training data, where the second prediction model enables determining whether an exploit to be developed for software vulnerabilities will be used in an attack.