Abstract:
As a result of the accelerated development and expansion of technology in the
present day, a new concern has emerged: cyberattacks. This has generated significant
concern across various domains globally, leading to considerable disruption in
networks and presenting PC users with a multitude of challenges. Presently, a
multitude of organisations are striving to combat these types of cyber-attacks through
the implementation of novel detection and subsequent destruction methods. The
domain of machine learning enables computers to acquire knowledge and skills
without requiring explicit programming. There are an abundance of implementation
strategies for this technology. This study aims to demonstrate a diverse array of
algorithms utilised in the defence against various cyber-attacks. This paper will
examine various classification algorithms utilised to defend against diverse cyber-
attacks, as well as the methods of defence against these attacks. The implementation,
accuracy, and testing time of these algorithms will vary depending on the classification
of the attack. This thesis will discuss various varieties of these algorithms.
