A Hybrid Intelligent System for IP Traffic Classification

The classification of IP traffic is important for many reasons, including network management and security, quality of service (QoS) monitoring and provisioning, and high hardware utilisation. Recently, many machine learning-based IP traffic classifiers have been developed. Unfortunately, most of them need to be trained on large datasets and thus require a long training time and significant computational power. In this paper, I investigate this problem and, as a solution, present a hybrid system, which I call the ISITC, that combines the random forest (RF) and XGBoost (XGB) machine learning techniques with the support vector classifier (SVC) as the final estimator, the stacking classifier. This design leads to the development of a model that performs the classification of IP traffic and internet applications efficiently and with high accuracy. I evaluate the performance of the ISITC and various IP traffic classifiers, including neural network (NN), RF, decision tree (DT), and XGB classifiers and SVCs. The experimental results show that the ISITC provides the best IP traffic classification, with an accuracy of 96.7, and outperforms the other IP traffic classifiers: the NN classifier has an accuracy of 59, the RF classifier has an accuracy of 88.5, the DT classifier has an accuracy of 90.5, the XGB classifier has an accuracy of 89.8, and the SVC has an accuracy of 64.8.

@article{Muslam2024,
  title     = {A Hybrid Intelligent System for IP Traffic Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Muhana Magboul Ali Muslam},
  doi       = {10.14569/IJACSA.2024.0150973},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150973}
}