  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 highly 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 into machine learning models. This aims to make
the detection complexity low enough for real-time operations, which is
particularly vital in any intrusion detection systems. This paper provides a
comprehensive comparison between these two feature reduction methods of
intrusion detection in terms of various performance metrics, namely, precision
rate, recall rate, detection accuracy, as well as runtime complexity, in the
presence of the modern UNSW-NB15 dataset as well as both binary and multiclass
classification. For example, in general, the feature selection method not only
provides better detection performance but also lower training and inference
time compared to its feature extraction counterpart, especially when the number
of reduced features K increases. However, the feature extraction method is much
more reliable than its selection counterpart, particularly when K is very
small, such as K = 4. Additionally, feature extraction is less sensitive to
changing the number of reduced features K than feature selection, and this
holds true for both binary and multiclass classifications. Based on this
comparison, we provide a useful guideline for selecting a suitable intrusion
detection type for each specific scenario, as detailed in Tab. 14 at the end of
Section IV.
