Specialized LLM Transfer Learning for Network Traffic Analysis
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Solution Overview
Problem
Traditional network traffic analysis methods, including manual examination and existing machine learning-based solutions, are time-consuming, error-prone, and lack adaptability to effectively address specific network issues, and existing machine learning-based solutions lack adaptability to effectively address specific network issues and existing machine learning-based solutions lack adaptability to effectively address specific network nuances and error characteristics.
Innovation Solution
A specialized large language model is generated through transfer learning on a base large language model using network traffic capture files, enabling efficient communication network analysis by performing masked language modeling and next sentence prediction, and incorporating transfer learning techniques such as LoRA, QLoRA, fine-tuning, and domain adaptation to enhance accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual examination methods are used for network traffic analysis, then skilled personnel can perform detailed analysis, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical examination with an automated deep learning system that uses convolutional neural networks to analyze network traffic PCAP files. The system automatically detects errors, anomalies, and patterns in network traffic without requiring skilled human personnel to manually examine raw data, thereby eliminating time consumption and human error while maintaining high detection accuracy.
Solution Approach 2:
The deep learning system performs self-service by automatically analyzing network traffic data without requiring continuous human intervention. Once trained, the model independently processes PCAP files, identifies errors, and generates analysis results, freeing skilled personnel from repetitive manual analysis tasks while maintaining consistent accuracy across all analyses.
2Productivity
If existing machine learning-based solutions are used for network traffic analysis, then automation is achieved, but the models lack adaptability to specific network nuances and error characteristics
Solution Approach 1:
The patent applies local quality by training the deep learning model on specific local network traffic data (PCAP files) that capture the unique characteristics, protocols, and error patterns of particular network environments. This allows the model to adapt to local network nuances and specific error characteristics rather than relying on generic pre-trained models, thereby improving both automation efficiency and adaptability to specific network contexts.
Solution Approach 2:
The system performs preliminary action by pre-training the deep learning model on labeled network traffic data before deployment. This preliminary training phase allows the model to learn specific network patterns, protocols, and error characteristics of the target environment, enabling it to quickly adapt and perform accurate automated analysis when deployed without requiring extensive customization later.
3Ease of manufacture
If pre-trained models are used for network traffic analysis, then deployment is simplified, but accuracy in detecting specific network errors is reduced
Solution Approach 1:
The patent applies parameter changes by fine-tuning the pre-trained deep learning model using domain-specific network traffic data. The model's parameters (weights and biases) are adjusted during training on local PCAP files to optimize detection accuracy for specific network errors and patterns. This maintains the ease of deployment from using pre-trained models while significantly improving accuracy through parameter adaptation to the specific network environment.
Data Source
AI summary
Embodiments relate to generating specialized large language models by performing transfer learning on a base large language model. The base large language model is trained using network traffic capture files as training data to predict information in a network traffic capture file during inference. The base large language model is modified into specialized large language models for including in different applications for performing communication network analysis. In this way, the specialized large language models may be developed in an expedient and efficient manner by leveraging the training performed on the base large language model.