Automated System for Detecting Cyber Bot Attacks in 5G Networks using Machine Learning

Abstract

The automated system for detecting cyber bot attacks in 5G networks relies on cloud servers to store data, facilitating the global access necessary for online transactions and services, but points to the rise of cybercrime with information security flaws and human stealth Attackers known as "Botmasters" spread Trojan malware to grow bots on the network causing DDOS attacks. Botnets are compromised computer networks controlled by attackers that are visible
for this reason. Machine learning algorithms have been proposed to identify bot networks with a focus on extracting features from high-dimensional datasets. However, the literature pays little attention to selection methods, which are crucial for developing effective machinelearning models.