Cyber Threat News Classification With BERT and Vector Embeddings
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Solution Overview
Problem
The escalating frequency and complexity of cyber threats across various sectors necessitate a cost-effective and efficient method for categorizing cyber-related content from news articles, as existing large language models require substantial computational resources and high costs, and there is a gap in applying BERT-based models for real-time news sources.
Innovation Solution
A cost-efficient framework using a finetuned BERT architecture with minimal training data, specifically the Cyber Activity News Alerting Language (CANAL) model, categorizes cyber-related news into five distinct categories, leveraging a specialized vector database and contextual embeddings, and periodically updates with emerging terminologies through a cyber signal discovery module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to categorize cyber-related content, then classification accuracy is improved, but computational resource requirements and costs increase substantially
Solution Approach 1:
The patent replaces expensive large language models with a more economical BERT-based model that can be fine-tuned with minimal data. This smaller model achieves comparable classification accuracy while consuming significantly fewer computational resources, effectively substituting a costly solution with a more efficient alternative.
Solution Approach 2:
The patent changes the parameters of the machine learning model by fine-tuning a pre-trained BERT model on a small dataset of cyber-related news articles. This parameter adjustment allows the model to achieve high classification accuracy for cybersecurity content without requiring the substantial computational resources of larger language models.
2Productivity
If BERT-based models are applied to real-time news sources, then classification efficiency is improved, but there is a gap in handling evolving cybersecurity terminologies
Solution Approach 1:
The patent applies preliminary action by fine-tuning the BERT model on a curated dataset of cyber-related news articles before deployment. This pre-training on domain-specific data enables the model to efficiently classify real-time news while maintaining the capability to adapt to evolving terminologies through periodic retraining with new data.
Solution Approach 2:
The patent implements dynamics by designing a system that can be periodically retrained with new cyber-related news articles. This allows the model to adapt its parameters and continue handling evolving cybersecurity terminologies effectively, making the system flexible and up-to-date with emerging threats and language.
3Loss of time
If minimal training data is used for fine-tuning, then cost and time are reduced, but model accuracy may be compromised
Solution Approach 1:
The patent uses preliminary action by starting with a pre-trained BERT model that has already learned general language patterns from large corpora. This pre-training provides a strong foundation that requires minimal additional fine-tuning on cyber-related data, thereby reducing training time while maintaining the ability to achieve high accuracy on domain-specific classification tasks.
Data Source
AI summary
Classifying cybersecurity signals from media sources into distinct categories is provided. The method comprises receiving a first data subset comprising data points labeled by subject matter experts according to a predetermined number of specified categories. The data points include information regarding cybersecurity from a set of news articles. The first subset is enriched by applying a random forest algorithm to generate synthetic data points, thereby deriving a second data subset that is augmented from the first subset. The combined first and second data subsets comprise an enhanced training dataset. A BERT model is trained with the enhanced training dataset to classify cybersecurity-related news according to the specified categories. The BERT model utilizes a specialized vector database integrating domain-specific cyber-related terminology and contextual embeddings. The trained BERT model classifies a second set of news articles according to the specified categories. The classification accounts for evolving cybersecurity terminologies and threat landscapes.