LLM-Based Content Classification for Network Security
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Solution Overview
Problem
Current methods for content classification in cloud environments are time-consuming, labor-intensive, and delay the classification of potentially risky content, requiring large teams and manual review.
Innovation Solution
Utilizing Large Language Models (LLMs) to convert tabular data related to networking and computer security into natural language, which is then input into the LLM to generate outputs that can be used to train machine learning models for tasks such as malware detection, intrusion detection, and content classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content classification methods are used, then classification accuracy can be maintained through human review, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent introduces LLMs as an intermediary between raw tabular data and final classification decisions. The LLM converts structured data into natural language descriptions, enabling automated analysis while maintaining accuracy previously achievable only through manual review. This intermediary processing step eliminates the need for human reviewers while preserving classification quality.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated LLM-based system. Instead of human analysts manually examining and categorizing content, the system uses LLMs to perform natural language processing and classification automatically, eliminating labor-intensive manual operations while maintaining or improving accuracy.
2Reliability
If large dedicated teams perform content classification, then comprehensive security coverage is achieved, but operational costs increase significantly
Solution Approach 1:
The patent implements a self-service classification system where LLMs automatically process and categorize content without requiring human intervention. The system serves itself by converting tabular data to natural language and performing classification autonomously, eliminating the need for large dedicated teams while maintaining comprehensive security coverage.
Solution Approach 2:
The patent fundamentally changes the operational parameters of the classification system by transitioning from human-based processing to LLM-based automated processing. This parameter change reduces operational costs dramatically while maintaining or improving security coverage, as the automated system can process content more efficiently without human resource constraints.
3Measurement precision
If traditional machine learning models are trained manually, then model performance can be optimized, but the training process is slow and resource-intensive
Solution Approach 1:
The patent replaces traditional manual model training mechanisms with LLM-driven automated training. Instead of relying on manual feature engineering and slow traditional ML training processes, the system uses LLMs to convert tabular data into natural language representations that can be processed more efficiently, accelerating model training while maintaining or improving performance.
Solution Approach 2:
The patent changes the training parameters by introducing natural language processing through LLMs into the traditional ML training pipeline. This parameter change enables faster data processing and model convergence, improving training speed without sacrificing model performance, as the LLM-enhanced approach can extract more meaningful features from the data.
Data Source
AI summary
Systems and methods for utilizing Large Language Models (LLMs) for improving machine learning models in network and computer security include obtaining tabular data related to an aspect of networking and computer security; converting the tabular data to natural language for each row in the tabular data; inputting the natural language for each row in the tabular data into a Large Language Model (LLM); obtaining an output from the LLM for each row in the tabular data with embedded data therewith; and utilizing the output to train a machine learning model related to the aspect of networking and computer security