Dark Web Threat Persona Analysis Using LLM Preprocessing
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Solution Overview
Problem
Existing large language models struggle to effectively analyze and comprehend dark web posts due to their irregular and cryptic nature, including non-textual characters and ungrammatical structure, making it difficult to identify potential security risks in a timely and accurate manner.
Innovation Solution
A method and system that preprocesses dark web posts by translating, removing noise, and tokenizing them to make them understandable by a threat analyzer LLM, using a prompt engineered to summarize and categorize posts into threat personas, with a post classifier ML model to filter relevant content, and store annotated posts for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dark web posts are analyzed directly by large language models, then analysis speed is maintained, but analysis accuracy deteriorates due to irregular and cryptic nature of dark web content
Solution Approach 1:
The patent applies preliminary action by preprocessing dark web posts before analysis. The system performs translation, noise removal, and tokenization on raw dark web posts to convert them into a standardized format that large language models can effectively process. This preprocessing step prepares the data in advance, ensuring that when the LLM analyzes the content, it receives clean, structured input that improves analysis accuracy while managing processing complexity.
2Measurement precision
If multiple preprocessing steps are applied to dark web posts, then analysis accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the preprocessing pipeline into distinct, modular steps: translation, noise removal, and tokenization. Each step handles a specific aspect of data cleaning independently. This segmentation allows the system to process different aspects of dark web posts in parallel where possible, and makes it easier to optimize individual steps without affecting the entire pipeline, thereby reducing overall processing time while maintaining categorization accuracy.
3Productivity
If dark web posts are analyzed at scale, then threat identification capability is improved, but computational resources required increase
Solution Approach 1:
The patent applies preliminary action by performing translation, noise removal, and tokenization on dark web posts before they reach the large language model for analysis. This preprocessing prepares the data in advance in a standardized format, which reduces the computational burden on the LLM during the actual analysis phase. By doing the heavy lifting of data cleaning and standardization beforehand, the system can process more posts through the LLM with lower per-unit computational cost, thereby improving threat identification throughput while managing resource consumption.
Data Source
AI summary
A method includes obtaining multiple raw dark web posts. The multiple raw dark web posts are preprocessed to obtain a tokenized dark web post set. A prompt is constructed for a threat analyzer large language model (LLM). The prompt includes at least a first tokenized dark web post a set of threat personas, each threat persona including a category and a definition, and an instruction to categorize the first tokenized dark web post with a threat persona and generate a first explanation corresponding to the categorization of the first tokenized dark web post. The prompt is processed by the threat analyzer LLM, to obtain a first annotated post and a corresponding first threat persona category. The first annotated post includes a post summary corresponding to the first tokenized dark web post, and the first explanation. The first explanation is based on a first threat persona definition.