Cybersecurity Language Model Training for Non-Linguistic Token Handling
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Solution Overview
Problem
Language models trained with self-supervised learning on cybersecurity documents are negatively affected by the high presence of non-linguistic elements, which are complex and arbitrary, leading to inefficiencies in learning and performance.
Innovation Solution
A system and method that classify non-linguistic elements in cybersecurity documents into completely non-linguistic and paralinguistic elements, allowing the language model to focus on linguistic elements by replacing or masking only completely non-linguistic elements, and training to predict and classify these elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If self-supervised learning is applied to cybersecurity documents containing non-linguistic elements, then the language model can process cybersecurity data, but the performance deteriorates due to complex and arbitrary non-linguistic elements
Solution Approach 1:
The patent segments cybersecurity documents into linguistic elements and non-linguistic elements, applying different processing strategies to each. Linguistic elements are used for self-supervised learning while non-linguistic elements are excluded or separately handled, resolving the contradiction between processing cybersecurity data and maintaining model performance
Solution Approach 2:
The patent applies different quality standards to different parts of the document: linguistic elements receive full processing attention for learning, while non-linguistic elements are filtered out or given minimal processing, allowing the model to focus on meaningful patterns without being degraded by arbitrary strings
2Loss of information
If non-linguistic elements are included in training data, then more cybersecurity information is captured, but learning efficiency decreases due to meaningless strings
Solution Approach 1:
The patent extracts and removes non-linguistic elements from the training data before applying self-supervised learning. This extraction process eliminates meaningless strings that reduce learning efficiency while preserving linguistic elements that contain actionable cybersecurity information
Solution Approach 2:
The patent converts the presence of non-linguistic elements from a harmful factor into a benefit by using them as a basis for classification tasks. The model learns to identify and categorize different types of non-linguistic elements (IP addresses, hashes, URLs), turning previously harmful noise into structured information that enhances overall system capability
3Measurement precision
If the language model processes all elements in cybersecurity documents, then comprehensive analysis is achieved, but training complexity increases due to arbitrary strings
Solution Approach 1:
The patent performs preliminary classification of document elements before the main language modeling training. Non-linguistic elements are identified and categorized in advance, allowing the training process to focus only on linguistic elements, thereby reducing training complexity while maintaining comprehensive analysis capability
Data Source
AI summary
Provided is a system for training a language model for cybersecurity, which includes: a document collection unit that collects a cybersecurity document used for training a language model for cybersecurity; an extraction unit that identifies non-linguistic elements in the cybersecurity document based on a non-linguistic element database; a tokenization unit that tokenizes the cybersecurity document to generate a plurality of tokens; and a language model application unit that controls the language model to simultaneously perform a first task of classifying types of the non-linguistic elements including at least one of a Bitcoin address, a hash value, an IP address, and a vulnerability identifier included in the cybersecurity document and a second task of recovering only linguistic elements of the cybersecurity document.