Cybersecurity Event Translation Using a Common Threat Ontology
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Solution Overview
Problem
Cybersecurity systems from different vendors use proprietary languages, complicating defense and orchestration, and existing NLP technologies struggle with the complexity and variability of cybersecurity data, necessitating a common ontology for effective translation and automation.
Innovation Solution
A Cyber-Specific Language Processing (CSLP) system using NLP and AI to translate cybersecurity event data into a common ontology, enabling high-volume data ingestion, enrichment, and platform-agnostic reporting, with automated schema updates and compatibility for diverse vendor systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cybersecurity systems from different vendors use proprietary languages, then each vendor can optimize their specific technology, but defenders must become experts in many dialects and data complexity increases
Solution Approach 1:
The patent introduces a common ontology as an intermediary layer between vendor-specific proprietary languages and defensive analytics. This ontology acts as a standardized intermediate representation that translates diverse vendor dialects into a unified format, reducing data complexity while preserving vendor-specific optimization capabilities.
Solution Approach 2:
The common ontology serves multiple functions: it translates vendor-specific data, enables cross-vendor analytics, and provides a unified interface for defensive systems. This universal framework allows the system to handle diverse proprietary languages without requiring defenders to learn each dialect.
2Adaptability or versatility
If existing NLP technologies are used for cybersecurity data translation, then general language processing is available, but they struggle with the complexity and variability of cybersecurity data
Solution Approach 1:
The patent applies local quality by creating a cybersecurity-specific ontology that tailors the translation framework to the unique characteristics of security data. Rather than using generic NLP, the system incorporates domain-specific schemas, event types, and relationships that reflect the actual structure and semantics of cybersecurity telemetry.
Solution Approach 2:
The system changes the parameters of language processing by moving from general-purpose NLP to a specialized ontology-driven approach. This involves transforming the translation task from statistical pattern matching to rule-based semantic mapping with domain-specific constraints, improving reliability for cybersecurity data.
3Adaptability or versatility
If vendor-specific frameworks are used for data reporting, then each vendor can maintain their own data model, but orchestration and automation are limited by data variability
Solution Approach 1:
The common ontology serves as a mediator that enables automation by providing a standardized intermediate format. Vendor-specific data models remain independent, but their outputs are translated into the common ontology, which then serves as the basis for automated orchestration and response actions.
Solution Approach 2:
The patent segments the data translation process into distinct layers: vendor-specific data models, common ontology translation layer, and automated analytics layer. This segmentation allows each layer to operate independently while maintaining interoperability, enabling both vendor independence and automation.
Data Source
AI summary
A Cybersecurity Event Handling Processor (CEHP) and method for processing security alerts includes: a File System containing a Universal Target Schema (UTS) of target language representations (UTS JSONs); a Normalizer running Feature Extraction and Word Embeddings algorithms; a Tree Converter; and a Transformer running linguistic and structural matching algorithms. The CEHP: (a) captures threat events in one or more native formats generated by cybersecurity tools; (b) runs Feature Extraction and Word Embeddings algorithms for tokenization and categorization of the captured events to create normalized events; (c) converts the normalized events into trees and then translates the trees into event representations in JSON (or XML) format (Event JSONs); and (d) runs nearest neighbor and/or linguistic and structural matching algorithms to compare the Event JSONs to the UTS JSONs to generate output JSONs (Translation JSONs) from the UTS corresponding to the captured events.