An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

Abstract

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.