Template-Type: ReDIF-Article 1.0
Author-Name:M.  Muntazir  Khan,  Muhammad  Ishaq,  Zubair  Ahmad  Shams,  Haseeb  Ullah  Jan,  M. Ghayoor Jan1Hussan Fatima
Author-Email:muntazirkhan131@gmail.com
Author-Workplace-Name:Institute  of  Computer  Sciences  and  Information  Technology  (ICS/IT), The  University  of Agriculture, Peshawar, Pakistan, Department of computer software engineering, The university of engineering and technology Mardan, Faculty   of   Engineering   and   Computing,   National   University   of   Modern   Languages Islamabad, Pakistan
Title:Network Traffic Classification in SDN Networks Using PCA Integrated Boosting Algorithms
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  theproposed  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. Followingthat, 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 integrationof 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 approximately10%.
Keywords:Network, Classification, SDN, XGBM, LGBM, PCA, confusion matrix
Journal:International Journal of Innovations in Science and Technology
Pages:856-870
Volume:7
Issue:2
Year: 2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1248/1897
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File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1248
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Handle: RePEc:abq:IJIST1:v:7:y:2025:i:2:p:856-870