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Title	A Comparison Of Feature Selection And Feature Extraction In Network Intrusion Detection Systems
ID_Doc	831
Authors	Vuong T.-C.; Tran H.; Trang M.X.; Ngo V.-D.; Luong T.V.
Year	2022
Published	Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
DOI	http://dx.doi.org/10.23919/APSIPAASC55919.2022.9979923
Abstract	Internet of Things (loT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, loT de-vices 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 loT 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 partic-ularly 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. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).
Author Keywords	feature extraction; feature se-lection; internet of things; Intrusion detection; machine learning; PCA; UNSW-NB15

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