Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset

Abstract

This paper considers the use of Synthetic Minority Oversampling Technique (SMOTE), Principal Component Analysis (PCA), and Ensemble Feature Selection (EFS) to improve the performance of AdaBoost-based Intrusion Detection System (IDS) on the latest and challenging CIC IDS 2017 Dataset [1]. Previous research [1] has proposed the use of AdaBoost classifier to cope with the new dataset. However, due to several problems such as imbalance of training data and inappropriate selection of classification methods, the performance is still inferior. In this research, we aim at constructing an improvement performance intrusion detection approach to handle the imbalance of training data, SMOTE is selected to tackle the problem. Moreover, Principal Component Analysis (PCA) and Ensemble Feature Selection (EFS) are applied as the feature selection to select important attributes from the new dataset. The evaluation results show that the proposed AdaBoost classifier using PCA and SMOTE yields Area Under the Receiver Operating Characteristic curve (AUROC) of 92% and the AdaBoost classifier using EFS and SMOTE produces an accuracy, precision, recall, and F1 Score of 81.83 %, 81.83%, 100%, and 90.01% respectively.