Decision tree based intrusion detection system for NSL-KDD dataset

Abstract

In this paper, Decision Tree (DT) based IDS is proposed for NSL-KDD dataset. The proposed work uses Correlation Feature Selection (CFS) subset evaluation method for feature selection. Feature selection improves the prediction performance of DT based IDS. Performance is evaluated before feature selection and after feature selection for five class classification (normal and types of attack) and binary class classification (normal and attack). The obtained result is compared and analyzed with the other reported techniques. The analysis shows that the proposed DT based IDS provides high DR and accuracy. The overall result for binary class classification for the dataset is higher than five class classification.