Voting Classification Model for Network Traffic Classification

Abstract

Abstract: Network traffic classification has produced incredible concentration in the academic world alongside the industrial domain. A few procedures have been recommended and created in the course of the most recent twenty years. This segment makes a discussion on various classification strategies and partitions them into four classes dependent on their ordered development. The network traffic classification has various phases which include pre-processing, feature extraction and classification. In the previous year’s various machine techniques is designed for network traffic classification. The techniques which are already designed give low accuracy. In this research work, voting classification method is designed for network traffic classification which give high accuracy. The proposed model is implemented in python and results is analyzed in terms of accuracy, precision and recall.