Comparative Analysis of Traditional and Ensemble Learning Models for Network Security System

In this paper use CIC-IDS2017 dataset to illustrate a comparative analysis of traditional and proposed models for intrusion detection in network security systems. The comparison includes DT, RF, ET and XGBoost classifiers. Normalize class, SMOTE Data preprocessing to compensate for missing values and of imbalance classes From the results of evaluation metrics, we conclude that proposed methods show a significant improvement on its model performances in detecting network intrusions as compared to traditional models XGboost specifically this work. Out of all the algorithms, the accuracy for XGBoost stands out, as it is mostly known to cater to complex attack patterns. The results indicate that ensemble base techniques offer the best solutions which can prove to be both effective and efficient as an intrusion detection systems for current network technologies.