Neural Language Modeling for Vulnerability Exploit Prediction
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Solution Overview
Problem
Conventional cybersecurity methods fail to accurately predict which software vulnerabilities will be exploited, as they primarily focus on word frequencies and lack context analysis, leading to data sparsity and high dimensionality issues, and existing scoring systems like CVSS are ineffective in distinguishing between threatening and non-malicious discussions.
Innovation Solution
A computer-implemented exploit-prediction framework that uses a neural language modeling approach to learn low-dimensional context-based distributed representations of darkweb/deepweb discussions, incorporating the paragraph vector algorithm to generate features that capture the meaning and context of textual information, and combines these with CVSS scores and ExploitDB data for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text analysis methods focusing on word frequencies are used, then the analysis process is simple, but the prediction accuracy is low and data sparsity occurs
Solution Approach 1:
The patent replaces conventional mechanical text analysis methods (word frequency counting) with neural language modeling algorithms that learn distributed representations. This substitution enables the system to capture contextual semantics and linguistic regularities, significantly improving prediction accuracy from baseline levels to 10% outperformance while managing complexity through automated learning processes.
Solution Approach 2:
The patent transforms the representation parameters of textual data from simple word frequencies to low-dimensional context-based distributed representations (embeddings). This parameter change allows the system to capture semantic meaning and contextual relationships, resolving the data sparsity problem while maintaining computational feasibility through dimensionality reduction.
2Loss of information
If conventional text analysis methods are used, then the computational resources required are low, but the ability to capture context and semantic meaning is insufficient
Solution Approach 1:
The patent changes the dimensional parameters of text representation from high-dimensional word frequency vectors to low-dimensional distributed representations. This transformation reduces computational resource requirements while preserving and enhancing context capture capabilities through semantic embeddings that encode linguistic regularities and contextual relationships.
Solution Approach 2:
The patent substitutes resource-intensive conventional text analysis with neural language modeling that learns efficient distributed representations. This substitution captures contextual information and semantic meaning more effectively while managing computational resources through the inherent efficiency of distributed representation learning and dimensionality reduction.
3Measurement precision
If standard scoring systems like CVSS are used, then the assessment process is straightforward, but the distinction between threatening and non-malicious discussions is ineffective
Solution Approach 1:
The patent replaces straightforward but ineffective CVSS scoring with neural language modeling that analyzes contextual semantics. This substitution enables precise distinction between threatening and non-malicious discussions by capturing linguistic regularities and contextual patterns, achieving 10% outperformance over conventional methods while maintaining operational simplicity through automated classification.
Solution Approach 2:
The patent transforms the assessment parameters from standardized CVSS scores to context-based distributed representations that capture semantic meaning. This parameter change enables more accurate threat assessment by considering contextual factors and linguistic patterns, effectively distinguishing malicious from non-malicious discussions while the automated nature maintains ease of operation.
4Measurement precision
If features from multiple data sources are combined, then the prediction performance improves, but the feature dimensionality and complexity increase
Solution Approach 1:
The patent merges features from multiple data sources (darkweb discussions, CVSS scores, ExploitDB data) into a unified prediction framework. This combination improves prediction performance by 12% through integrating complementary information from diverse sources while managing complexity through the cohesive neural language modeling approach that processes heterogeneous data types together.
Solution Approach 2:
The patent applies dimensionality reduction through low-dimensional distributed representations to manage the complexity of combined features from multiple sources. This parameter transformation consolidates high-dimensional features from darkweb discussions, CVSS scores, and ExploitDB data into compact embeddings, improving prediction performance while controlling feature dimensionality and system complexity.
Data Source
AI summary
Various embodiments of a computer-implemented framework for predicting exploitation of software vulnerabilities are disclosed.