Semantic Model for Vulnerability Attack Chain Mapping
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Solution Overview
Problem
Defenders lack a concrete approach to prioritize and manage vulnerabilities (CVEs) in the context of attack chains due to the difficulty in classifying them into an attack chain taxonomy, leading to manual efforts that expose networks to potential threats.
Innovation Solution
A semantic model is used to parse textual descriptions of vulnerabilities and map them to stages of an attack chain taxonomy, generating labels based on context and distance functions to determine intrusion techniques, enabling automated vulnerability characterization and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of vulnerabilities into attack chain taxonomy is performed, then vulnerability prioritization accuracy is improved, but time consumption and labor cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated semantic model that uses natural language processing to map vulnerability descriptions to attack chain taxonomy stages. The semantic model parses vulnerability text, extracts features, and automatically determines the appropriate attack stage without human intervention, thereby eliminating time loss while maintaining classification accuracy.
Solution Approach 2:
The semantic model enables the vulnerability classification system to serve itself by automatically processing vulnerability descriptions and assigning taxonomy labels without requiring manual analyst input. The system uses pre-trained semantic understanding capabilities to autonomously perform the classification task that previously required human experts.
2Reliability
If comprehensive vulnerability analysis is performed to understand attacker actions, then risk assessment quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the vulnerability analysis process into distinct components: text parsing, feature extraction, semantic labeling, and taxonomy mapping. By dividing the comprehensive analysis into modular stages, the system maintains high risk assessment quality while reducing overall processing complexity through structured, step-wise evaluation.
Solution Approach 2:
The semantic model acts as an intermediary layer between raw vulnerability descriptions and the attack chain taxonomy. It translates unstructured vulnerability text into structured semantic labels that map to taxonomy stages, simplifying the connection between detailed vulnerability analysis and high-level risk assessment without losing critical information.
3Productivity
If automated semantic modeling is implemented for vulnerability mapping, then processing efficiency is improved, but model complexity increases
Solution Approach 1:
The semantic model is pre-trained on extensive vulnerability data and attack chain taxonomy before deployment. This preliminary training phase enables the model to automatically understand and map vulnerability descriptions to attack stages without requiring complex runtime processing, thereby achieving high processing efficiency while the model complexity is contained during the training phase.
Data Source
AI summary
In an embodiment, a semantic model and a semantic model training method that obtains a textual description of one or more features associated with a first vulnerability that has been used in one or more attacks. Text is parsed from the first textual description in accordance with one or more rules. The system determines a first label for the first vulnerability that is associated with one or more of a plurality of stages of an attack chain taxonomy. The model is generated or refined to map the parsed text to the first label associated with the one or more stages of the attack chain taxonomy.