Network Traffic Analysis and Network Modal Mapping Method

This document presents a framework for network traffic classification and modality mapping based on large language models (LLMs), addressing the inefficiencies of traditional methods in dynamic network environments. The proposed approach automates multi- dimensional traffic feature extraction and intelligent decision- making to achieve precise alignment between traffic patterns and computing-storage-transmission requirements. The framework comprises two phases: pre-training (generating multi-modal traffic representations from pcap data) and mapping (dynamically formulating resource allocation strategies). It supports anomaly detection, QoS assurance, and multi-service collaboration, thereby significantly enhancing resource utilization efficiency and network service performance.