International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

A MACHINE LEARNING APPROACH FOR NETWORK TRAFFIC CLASSIFICATION

Author : Isha ¹ Prof. Jasbir Singh Saini ² Prof. Kamaldeep Kaur ³

Date of Publication :31st July 2021

Abstract: The network traffic classification task is focused on recognizing diverse varieties of applications or traffic specifics for which the sustained data packets get analysed that is indispensable in transmission networks in these days. The traffic can be classed in several phases, in which the step of preprocessing get achieved, extracted and classification of the attributes is performed. Meanwhile, the processing of dataset is carried out as it is taken as input in the process of classification. The dataset is split into two positions, which are named as training and testing. The training set includes 70% of the entire dataset and testing set has 30%. The voting classification method is implemented in which the KNN (K-Nearest Neighbour) is integrated with the RF (Random forest) and SVM (Support Vector Machine). The suggested method is deployed in the programming language python and several parameters instance as recall, precision and accuracy are taken in the account for quantifying the outcome. This indicates that suggested method yields higher accuracy, precision and recall in comparison with the traditional classification models.

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