Software Weakness Weaponization Prediction Models
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Solution Overview
Problem
Organizations face challenges in predicting whether a software weakness will be used in an exploit or malware and estimating the probability of digital compromise, given the complexity of managing multiple heterogeneous hardware platforms and disparate software products.
Innovation Solution
A method utilizing statistical sampling, clustering, classification, regression, reinforcement learning, and survival analysis to generate models that predict the likelihood of software weaknesses being integrated into exploits or malware, and the subsequent probability of digital compromise, involving preprocessing transformations, model selection, and statistical sampling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical sampling and machine learning models are used to predict software weakness weaponization, then prediction accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the prediction process into multiple independent machine learning models, each specializing in different aspects of software weakness analysis (e.g., exploit prediction, malware prediction, compromise prediction). This segmentation allows each model to focus on specific patterns while distributing computational complexity across multiple smaller models rather than one monolithic complex model.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and curating historical data before model training, including collecting software weakness data, exploit data, malware data, and compromise data into structured formats. This preliminary data preparation reduces the computational burden during actual prediction by having clean, organized data ready for rapid model inference.
2Measurement precision
If comprehensive data analysis is performed on software weaknesses, exploits, malware, and compromises, then risk estimation accuracy is improved, but data processing time and resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and curating historical data before model training, including collecting software weakness data, exploit data, malware data, and compromise data into structured formats. This preliminary data preparation reduces the computational burden during actual prediction by having clean, organized data ready for rapid model inference.
Solution Approach 2:
The patent creates simplified representations (copies) of complex security data through structured data models that capture essential features without requiring analysis of complete raw datasets. These data copies enable rapid prediction while preserving the critical information needed for accurate risk estimation.
3Adaptability or versatility
If multiple machine learning models are deployed to predict different aspects of security risk, then coverage of security threats is improved, but system complexity and model management overhead increase
Solution Approach 1:
The patent segments the prediction process into multiple independent machine learning models, each specializing in different aspects of software weakness analysis (e.g., exploit prediction, malware prediction, compromise prediction). This segmentation allows each model to focus on specific patterns while distributing computational complexity across multiple smaller models.
Solution Approach 2:
The patent creates a unified prediction system that integrates multiple specialized models into a single platform capable of predicting various security outcomes (exploit likelihood, malware likelihood, compromise probability). This universal system manages multiple models through standardized interfaces and data structures, reducing management overhead despite the diversity of prediction functions.
Data Source
AI summary
A method and/or computer software for estimating the probability that a software weakness will be used in an exploit and/or malware and the probability that the developed exploit and/or malware will result in a compromise.