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

• Main Authors: Thrupthi, C.P., Chitra, K., Harilakshmi, V.M.
• Format: Article
• Language: English; English
• Published: INTI International University 2024
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://eprints.intimal.edu.my/2068/1/joit2024_31.pdf; http://eprints.intimal.edu.my/2068/2/609; http://eprints.intimal.edu.my/2068/; http://ipublishing.intimal.edu.my/joint.html

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.