A Comprehensive Framework for Network Traffic Analysis and Prediction Using Synthetic Data, Machine Learning, and Interactive Visualization

This research introduces an approach that merges intelligence (AI) machine learning and interactive visualization to enhance network traffic analysis and forecasting. Initially the framework generates network structures that mimic diverse network scenarios and traffic trends. It then educates sophisticated machine learning algorithms to anticipate network behavior and performance based on these simulations. The setup features a user web interface that allows users to monitor real time network dynamics assess performance metrics and engage with the data through interactive tools to apply practical insights, from these models. With the integration of data, predictive analytics and dynamic visualization network administrators now possess a set of tools for, in depth performance monitoring and proactive network administration.

I. Introduction

The accurate prediction and analysis of network traffic are one of the cornerstones of efficient network management, effecting proactive strategies to prevent congestion and improving performance. Most traditional approaches are based on real-life traffic data, which can sometimes be very hard to obtain and not representative enough to capture all kinds of scenarios occurring on a network. This work presents a new, integrated framework that synergistically merges synthetic data generation, state-of-the-art machine learning techniques, and interactive visualization tools as a means to address these challenges. The configured wide spectrum of synthetic network environments and traffic patterns will be used to train the predictive models, which eventually provide the fine-grained understanding into performance. It provides integration with an interactive visualization interface, enabling real-time analysis and fully engaging users for a complete network management and optimization package.