A Survey on Wireless Network Traffic Analysis using Machine Learning Algorithms

Analyzing and forecasting network traffic for different applications has gained popularity in recent years. Several tests have been carried out to find issues with the computer network apps that are in use today. Anticipating and evaluating network traffic can assist in implementing preemptive steps to guarantee safe, dependable, and excellent network connection. Numerous methods, including data mining approaches and neural network-based techniques, have been suggested and assessed for the purpose of predicting patterns of network traffic. For this goal, other linear and nonlinear models have also been developed. The main objectives of combining different network analysis and prediction techniques are efficiency and optimization. The goal of this survey study is to present a thorough review of the variety of methods used in traffic prediction and network analysis. The paper delves into the distinctive features and findings of previous research efforts and brings to light the patterns that have emerged from these studies. Furthermore, it provides an overview of the various areas in which network traffic analysis and prediction have shown their effectiveness.