  Due to an exponential increase in the number of cyber-attacks, the need for
improved Intrusion Detection Systems (IDS) is apparent than ever. In this
regard, Machine Learning (ML) techniques are playing a pivotal role in the
early classification of the attacks in case of intrusion detection within the
system. However, due to a large number of algorithms available, the selection
of the right method is a challenging task. To resolve this issue, this paper
analyses some of the current state-of-the-art intrusion detection methods and
discusses their pros and cons. Further, a review of different ML methods is
carried out with four methods showing to be the most suitable one for
classifying attacks. Several algorithms are selected and investigated to
evaluate the performance of IDS. These IDS classifies binary and multiclass
attacks in terms of detecting whether or not the traffic has been considered as
benign or an attack. The experimental results demonstrate that binary
classification has greater consistency in their accuracy results which ranged
from 0.9938 to 0.9977, while multiclass ranges from 0.9294 to 0.9983. However,
it has been also observed that multiclass provides the best results with the
algorithm k-Nearest neighbor giving an accuracy score of 0.9983 while the
binary classification highest score is 0.9977 from Random Forest. The
experimental results demonstrate that multiclass classification produces better
performance in terms of intrusion detection by specifically differentiating
between the attacks and allowing a more targeted response to an attack.
