Dark Web BERT Pretraining for Ransomware Leak Site Detection
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Solution Overview
Problem
Existing cybersecurity technologies struggle to effectively analyze and understand the dark web due to its unique language patterns and non-linguistic elements, which complicates tasks such as ransomware leak site detection and threat thread classification.
Innovation Solution
A method and device utilizing a BERT-based language model that preprocesses dark web data, balances datasets, removes duplicates, and fine-tunes the model with labeled dark web data to enhance its performance in detecting ransomware leak sites and classifying threat threads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general language model is used for dark web analysis, then the model can process various languages, but it fails to accurately detect ransomware leak sites and classify threat threads due to lack of domain-specific knowledge
Solution Approach 1:
The patent applies preliminary action by pretraining the BERT model on extensive dark web corpus data before fine-tuning for specific tasks. This pretraining stage equips the model with domain-specific language patterns and vocabulary, enabling it to accurately detect ransomware leak sites and classify threat threads without requiring retraining from scratch.
Solution Approach 2:
The patent utilizes parameter changes by transitioning the model from a general-language state to a dark-web-specialized state through two-stage training. The model parameters are first updated with dark web data in unsupervised pretraining, then further refined with labeled task-specific data in fine-tuning, achieving optimal performance for cybersecurity tasks.
2Reliability
If raw dark web data is used directly for training, then the model can capture authentic language patterns, but the data contains duplicates, unbalanced categories, and noise that reduce model performance
Solution Approach 1:
The patent applies preliminary action by performing data preprocessing before model training. The raw dark web data is first cleaned of duplicates using similarity algorithms, then balanced across categories through sampling techniques, and finally tokenized and vectorized. This preliminary data preparation ensures high-quality input for model training while maintaining authentic language patterns.
3Measurement precision
If manual analysis of dark web content is performed, then detailed understanding of threat patterns is achieved, but the process is time-consuming and scalable only to limited volumes of data
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated BERT-based language model system. The model processes thousands of dark web posts, comments, and threat threads automatically, achieving both high accuracy in threat detection and scalability to large datasets. The BERT model captures contextual nuances and language patterns that would require extensive manual analysis.
Data Source
AI summary
Provided are a device and method for performing a task for cybersecurity on the basis of a dark web. The method performed by a device includes acquiring raw dark web data from a database, acquiring first dark web data by preprocessing the raw dark web data, pretraining a bidirectional encoder representations from transformers (BERT)-based language model using the first dark web data, fine-tuning the pretrained BERT-based language model using second dark web data, and performing a task for cybersecurity using the fine-tuned BERT-based language model.