N Manju; B S Harish; V Prajwal

Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier

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Author(s)

1. Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, 570006, India

2. Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, 570006, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2019.07.06

Received: 4 May 2019 / Revised: 20 May 2019 / Accepted: 28 May 2019 / Published: 8 Jul. 2019

Index Terms

Identification, Classification, Feature Selection, Internet Traffic

Abstract

Identification and classification of internet traffic is most important in network management to ensure Quality of Service (QoS). However, existing machine learning models tend to produce unsatisfactory results when applied with imbalanced datasets involving multiple classes. There are two reasons for this: the models have a bias towards classes which have more samples and they also tend to predict only the majority class data as features of the minority class are often treated as noise and therefore ignored. Thus, there is a high probability of misclassification of the minority class compared with the majority class. Therefore, in this paper, we are proposing an ensemble feature selection based on the tree approach and ensemble classification model using XGboost to enhance the performance of classification. The proposed model achieves better classification accuracy compared to other tree based classifiers.

Cite This Paper

N Manju, B S Harish, V Prajwal, "Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier", International Journal of Computer Network and Information Security(IJCNIS), Vol.11, No.7, pp.37-44, 2019. DOI:10.5815/ijcnis.2019.07.06

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International Journal of Computer Network and Information Security (IJCNIS)