An Investigation into the Performances of the State-of-the-art Machine
  Learning Approaches for Various Cyber-attack Detection: A Survey
Tosin Ige Christopher Kiekintveld Aritran Piplai
To secure computers and information systems from attackers taking advantage
of vulnerabilities in the system to commit cybercrime, several methods have
been proposed for real-time detection of vulnerabilities to improve security
around information systems. Of all the proposed methods, machine learning had
been the most effective method in securing a system with capabilities ranging
from early detection of software vulnerabilities to real-time detection of
ongoing compromise in a system. As there are different types of cyberattacks,
each of the existing state-of-the-art machine learning models depends on
different algorithms for training which also impact their suitability for
detection of a particular type of cyberattack. In this research, we analyzed
each of the current state-of-theart machine learning models for different types
of cyberattack detection from the past 10 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