Feature Selection Methods Simultaneously Improve the Detection Accuracy and Model Building Time of Machine Learning Classifiers

Abstract

The detection accuracy and model building time of machine learning (ML) classifiers are vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently, researchers have introduced feature selection methods to increase the detection accuracy and minimize the model building time of a limited number of ML classifiers. Therefore, identifying more ML classifiers with very high detection accuracy and the lowest possible model building time is necessary. In this study, the authors tested six supervised classifiers on a full NSL-KDD training dataset (a benchmark record for Internet traffic) using 10-fold cross-validation in the Weka tool with and without feature selection/reduction methods. The authors aimed to identify more options to outperform and secure classifiers with the highest detection accuracy and lowest model building time. The results show that the feature selection/reduction methods, including the wrapper method in combination with the discretize filter, the filter method in combination with the discretize filter, and the discretize filter, can significantly decrease model building time without compromising detection accuracy. The suggested ML algorithms and feature selection/reduction methods are automated pattern recognition approaches to detect network attacks, which are within the scope of the Symmetry journal.

Keywords

• IDS
• ML classifiers
• information security
• network
• Weka
• NSL-KDD