MLP Model Retraining for Vulnerability Exploit Prediction
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Solution Overview
Problem
Companies face challenges in predicting which computer vulnerabilities will be exploited due to the large number of potential vulnerabilities, limited resources, and the difficulty in allocating resources effectively, as only a small percentage may impact a company, but with potentially high damages. Additionally, machine learning models used for prediction often become stale due to the rapidly changing nature of vulnerability data.
Innovation Solution
An exploit prediction system utilizing a multilayer perceptron (MLP) model that automatically retrain using real-time data from databases, categorizing vulnerability content into training and inference data sets, and employing a hyperparameter optimization framework to select and test model configurations, ensuring the model remains updated and accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used for vulnerability exploitation prediction, then prediction capability is improved, but model accuracy deteriorates over time due to data changes
Solution Approach 1:
The patent implements dynamic model retraining by automatically updating the machine learning model with new vulnerability data at scheduled intervals or when data thresholds are met. This transforms the static model into a dynamic system that adapts to changing vulnerability landscapes, resolving the contradiction between initial prediction accuracy and long-term model freshness.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction results and new vulnerability data are continuously fed back into the model retraining process. This closed-loop feedback ensures the model learns from actual exploitation patterns and data evolution, maintaining both accuracy and reliability over time.
2Adaptability or versatility
If comprehensive vulnerability data is analyzed, then prediction coverage is improved, but computational resources are exhausted
Solution Approach 1:
The patent segments the vulnerability data processing into distinct phases: initial comprehensive training on all available data, and subsequent incremental updates using only new or changed data. This segmentation allows the system to achieve broad prediction coverage initially while consuming minimal computational resources during ongoing operations.
Solution Approach 2:
The system applies partial action by performing full model training only when necessary (initially or after significant data changes), and using lighter incremental updates for routine data additions. This approach maintains comprehensive coverage capability while avoiding excessive computational resource consumption during normal operations.
3Measurement precision
If frequent model retraining is performed, then model accuracy is maintained, but processing time increases
Solution Approach 1:
The patent implements periodic retraining with optimized intervals, combining scheduled retraining events with event-driven triggers (such as significant data changes or threshold crossings). This periodic action maintains model accuracy by retraining at appropriate intervals while avoiding unnecessary frequent retraining that would waste time.
Solution Approach 2:
The system changes the retraining parameters dynamically based on data characteristics, adjusting retraining frequency and intensity according to the rate of vulnerability data evolution. When data changes slowly, retraining occurs less frequently; when data evolves rapidly, retraining intensity increases, optimizing the balance between accuracy maintenance and time efficiency.
Data Source
AI summary
Systems and methods may be used to determine a computer vulnerability exploit via use of a machine learning model. Vulnerability content data may be received from one or more vulnerability feeds or data stores. The vulnerability content data may be categorized as either training data or inference data. A machine learning model may be trained based on the training data and then used to predict exploits of computer vulnerabilities identified in the inference data.