Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection. (arXiv:2205.06177v1 [cs.CR])

Machine Learning-based supervised approaches require highly customized and
fine-tuned methodologies to deliver outstanding performance. This paper
presents a dataset-driven design and performance evaluation of a machine
learning classifier for the network intrusion dataset UNSW-NB15. Analysis of
the dataset suggests that it suffers from class representation imbalance and
class overlap in the feature space. We employed ensemble methods using Balanced
Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest empowered
by Hellinger Distance Decision Tree (RF-HDDT). BB and XGBoost are tuned …

design detection intrusion intrusion detection network