Cybersecurity NER Model Using Semantic Similarity for Ambiguous Entities
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Solution Overview
Problem
Current Named Entity Recognition (NER) tools for cybersecurity information extraction rely heavily on feature engineering, which is time-consuming, domain-specific, and lacks accuracy when applied across different domains, especially in dynamic fields like cybersecurity.
Innovation Solution
A computer-implemented system and method for generating and implementing a NER model that includes a semantic similarity measure to automatically determine the category of unclassified words based on the context of the entire sentence, facilitating automatic dataset labeling and corpus generation for cybersecurity entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature engineering is used for NER tool construction, then domain-specific accuracy is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The system performs self-training by automatically generating training data from unstructured cybersecurity text through entity recognition and relationship extraction, eliminating the need for manual feature engineering and annotation. The model iteratively improves itself by learning from generated data, reducing construction time while maintaining domain-specific accuracy.
Solution Approach 2:
The system pre-processes unstructured cybersecurity text to generate structured training data with entities and relationships before model training. This preliminary data preparation automates what would otherwise require extensive manual feature engineering, reducing construction time while preserving domain expertise.
2Measurement precision
If manual feature selection and annotation is performed, then NER tool accuracy is improved, but productivity decreases
Solution Approach 1:
The system automatically generates training data with entity annotations and relationships without manual intervention. The model iteratively refines its own training dataset by extracting entities and relationships from unstructured text, eliminating manual annotation while maintaining high recognition accuracy through self-learning.
Solution Approach 2:
The system replaces manual mechanical annotation processes with automated computational methods. Machine learning models perform entity recognition and relationship extraction automatically, substituting human annotators with algorithms that scale indefinitely without reducing accuracy.
3Measurement precision
If domain-specific NER tools are developed, then accuracy in that domain is improved, but adaptability to other domains deteriorates
Solution Approach 1:
The system uses a universal machine learning framework that can be applied to any cybersecurity domain. The model learns domain-specific patterns from unstructured text without requiring domain-specific feature engineering, enabling it to adapt to different cybersecurity contexts (malware analysis, threat intelligence, vulnerability management) using the same core architecture.
Solution Approach 2:
The system dynamically adapts to different cybersecurity domains through iterative self-training on domain-specific unstructured data. The model adjusts its parameters and learned representations based on the specific domain data it processes, maintaining high accuracy across diverse cybersecurity applications without requiring domain-specific tool development.
4Speed
If dictionaries and look-ups are used for entity identification, then recognition speed is improved, but maintenance complexity increases in dynamic fields
Solution Approach 1:
The system replaces static dictionary look-ups with dynamic machine learning-based entity recognition. The model automatically learns entity patterns from unstructured cybersecurity text, eliminating the need for manual dictionary creation and maintenance while maintaining fast recognition speeds through optimized model inference.
Solution Approach 2:
The system uses dynamic, continuously learning models instead of static dictionaries. The entity recognition capability adapts automatically to new cybersecurity entities and patterns through iterative training on unstructured data, eliminating manual dictionary updates while maintaining fast recognition through efficient model architecture.
Data Source
AI summary
A computer-implemented system implements a named entity recognition (NER) model trained for automatic dataset labeling and corpus generation for cybersecurity entities. The NER model includes a semantic similarity measure to determine which category an unclassified/unlabeled word such as an ambiguous keyword with more than one meaning should belong to based on the semantic similarity of an entire sentence.