Network Intrusion Detection System Using XGBoost and Random Forest Algorithms
Agu Edward Onyebueke
Computer Science Department, Federal University Wukari, Nigeria.
Addakenjo Ali David *
Information and Communication Technology Center, Federal University Wukari, Nigeria.
Stephen Munu
Computer Science Department, Federal University Wukari, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Data mining is a relatively new discipline that arose in response to the proliferation of digital information. Security and privacy issues have gained increased attention as the internet's data storage capacity continues to grow. Problems with data theft and intrusion are a common source of frustration for users. In order to anticipate and identify intrusions, this study suggests developing a model with the XGBoost and Random Forest algorithms. Python Anaconda and Kaggle datasets (found at www.kaggle.com) are integral parts of the study methodology. The research uses the XGBoost and Random Forest algorithms on the UNSW-NB15 2017 and KDD datasets, respectively. The XGBoost algorithm performs admirably on the first dataset, with 100% accuracy, precision, and recall, and a perfect F1-score. In addition, on the second dataset, both algorithms attain near-perfect accuracy (99% and 98%, respectively), after the pre-processing stages (normalization, feature selection, scaling of the dataset) and the application of Synthetic Minority Over-sampling Techniques (SMOTE). These findings shed light on the algorithms' capabilities and how well they achieve the study's goals.
The results show that the XGBoost algorithm is the most accurate and dependable option for the datasets under consideration.
Keywords: Anomalous, random forest, overfitting, XGboost (eXtreme gradient boosting)