Specialized Large Language Models for Network Traffic Anomaly Detection
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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, failing to accurately capture specific network nuances and error characteristics, necessitating human intervention.
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 and accurate communication network analysis by performing tasks such as anomaly detection, failure prediction, and knowledge graph generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual examination methods are used for network traffic analysis, then analysis can be performed with simple tools, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical examination with an automated machine learning system. A base large language model is trained on network traffic capture files and then fine-tuned through transfer learning to automatically detect errors and anomalies, eliminating the need for manual inspection while improving both speed and accuracy.
Solution Approach 2:
The patent changes the parameters of the language model through transfer learning, adapting a pre-trained base model to specific network traffic analysis tasks. By fine-tuning the model with domain-specific training data and adjusting its parameters, the system achieves high accuracy in detecting network errors while maintaining fast automated processing.
2Extent of automation
If pre-trained machine learning models are used for network traffic analysis, then automation is achieved, but the models lack adaptability to specific network nuances
Solution Approach 1:
The patent performs preliminary training of a base large language model on general network traffic capture files before deploying it for specific analysis tasks. This pre-training establishes a foundation of automation capability that can then be rapidly adapted to specific network environments through transfer learning, combining the benefits of automation with domain-specific adaptability.
Solution Approach 2:
The patent makes the model dynamic by implementing transfer learning that allows the base model to adapt its parameters based on specific network traffic characteristics. The model can be fine-tuned for different network environments and task requirements, providing both automated processing and adaptability to specific network nuances.
3Productivity
If existing machine learning-based solutions are deployed, then some automation is achieved, but human intervention is still required for adaptation
Solution Approach 1:
The patent enables the system to perform self-service through automated transfer learning. The base large language model automatically adapts to specific network traffic analysis tasks by training on domain-specific data without requiring manual configuration or human intervention for adaptation, thereby maintaining high productivity while eliminating the need for human involvement in model customization.
4Measurement precision
If specialized models are trained from scratch for each network analysis task, then task-specific accuracy is improved, but the complexity and training time increase significantly
Solution Approach 1:
The patent performs preliminary training of a base large language model on general network traffic data before adapting it to specific tasks. This pre-established foundation reduces the complexity of task-specific model development, as subsequent specialized models can be created through efficient transfer learning rather than training from scratch, maintaining high task-specific accuracy while reducing overall system complexity.
Solution Approach 2:
The patent creates a universal base large language model that can serve multiple network analysis tasks through transfer learning. This single base model can be adapted to various specific tasks such as error detection, anomaly identification, and traffic pattern analysis, reducing the need for multiple specialized models and thereby decreasing device complexity while maintaining task-specific accuracy.
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.