Network Traffic Classification in SDN Networks Using PCA Integrated Boosting Algorithms

M. Muntazir Khan; Muhammad Ishaq; Zubair Ahmad Shams; Haseeb Ullah Jan; M. Ghayoor Jan; Hussan Fatima

Authors

M. Muntazir Khan Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan
Muhammad Ishaq Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan
Zubair Ahmad Shams Department of computer software engineering, The university of engineering and technology Mardan
Haseeb Ullah Jan Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan
M. Ghayoor Jan Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan
Hussan Fatima Faculty of Engineering and Computing, National University of Modern Languages Islamabad, Pakistan

Keywords:

Network, Classification, SDN, XGBM, LGBM, PCA, confusion matrix

Abstract

In recent years, internet traffic has increased as a result of the introduction of new services and apps. As a result, managing network traffic has grown more challenging. To accomplish this, several classification techniques for network traffic were proposed. Several researchers have used the most advanced deep learning and machine learning models for the suggested challenge. The suggested work can also make use of boosting methods. Boosting algorithms take advantage of the decision tree idea. They take little training time, and model training does not require a powerful system. Thus, boosting algorithms like Extreme Gradient Boosting Model (XGBM), Light Gradient Boosting Model (LGBM), Cat Boost, and Ada Boost with the integration of Principle component analysis (PCA) are used in the proposed study to classify network traffic. The results of these models are compared in terms of confusion matrix, accuracy, precision, recall, and F-Measure. The Network traffic android malware dataset, which was utilized in the proposed study, is publicly accessible online on Kaggle.com. For simulation, Python and its libraries such as sci-kit-learn, tensor flow, keras, and matplotlib are utilized. Following the simulation, the results showed that the XGBM had 90.41% accuracy, 96.39% precision, 89.72% recall, and 92.91% f-measures, while the LGBM had 89.02% accuracy, 90.04% precision, 89.8% recall, and 89.83% f-measures. 86.87% accuracy, 83.97% recall, 89.43% precision, and 86.61% f-measure were attained with Cat Boost. Following that, ada boost obtained 83.07% accuracy, 80% recall rate, 85.25 precision, and 82.58% f-measures. After the integration of the proposed boosting algorithms with PCA, we achieved a very significant enhancement in results. After the integration, it has been achieved that the accuracy rate of XGBoost has improved to 95.56%, while the recall rate is 94.39%, precision is 96.72% and the F-Measure rate has improved to 93.91%. Similarly, the performance of the light Gbm model is also improved with the integration of PCA. It achieved an accuracy rate of 93.41%, precision of 93.72%, recall of 92.39%, and f-measures of 92.91%. Following this, the performance of PCA integrated cat boost could also be seen as improved, as it achieved an accuracy rate of 94.41%, precision rate of 93.72%, recall of 92.39%, and F-measures of 93.91%. Similarly, the performance of a boost has also gained improvement by achieving an accuracy rate of 94.56%, precision rate of 94.72%, recall of 93.39%, and F-measure score of 93.91%. After all the simulations and performance evaluations, it has been achieved that the integration of PCA with the boosting algorithm is a simple trick to improve the performance of boosting algorithms. As here the performance of each model is improved to approximately 10%.

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