A Comparison of Feature Selection and Feature Extraction in Network Intrusion Detection Systems

Internet of Things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed to machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in intrusion detection systems. This paper provides a comprehensive comparison between these two methods in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of UNSW-NB15 dataset. Note that such a comparison between feature selection and feature extraction methods has been overlooked in the literature. Furthermore, based on this comparison, we provide a useful guideline on selecting a suitable intrusion detection type for each specific scenario.