Automated System for Detecting Cyber Bot Attacks in 5G Networks using Machine Learning
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 "...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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 |
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| Summary: | 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. |
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