  In this research, we analyzed the suitability of each of the current
state-of-the-art machine learning models for various cyberattack detection from
the past 5 years with a major emphasis on the most recent works for comparative
study to identify the knowledge gap where work is still needed to be done with
regard to detection of each category of cyberattack. We also reviewed the
suitability, effeciency and limitations of recent research on state-of-the-art
classifiers and novel frameworks in the detection of differnet cyberattacks.
Our result shows the need for; further research and exploration on machine
learning approach for the detection of drive-by download attacks, an
investigation into the mix performance of Naive Bayes to identify possible
research direction on improvement to existing state-of-the-art Naive Bayes
classifier, we also identify that current machine learning approach to the
detection of SQLi attack cannot detect an already compromised database with
SQLi attack signifying another possible future research direction.
