MACHINE LEARNING ALGORITHMS FOR CYBER ATTACK DETECTION AND CLASSIFICATION

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.