A Literature Review of Machine Learning Techniques for Cybersecurity in Data Centers

2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2021) 2021
Evita Roponena, Jānis Kampars, Andris Gailītis, Jānis Strods

Currently, information and communication technologies (ICT) are an essential part of almost any business sector and, therefore, are the target of different cyberattacks. ICT security measures are necessary to protect information from unauthorized access. These measures include network monitoring, threat detection, and prevention. Machine learning is an important part of cybersecurity solutions that allows us to automatically analyze data patterns and learn from them to prevent similar attacks or to predict possible threats. This paper provides a literature review that summarizes different machine learning techniques and feature sets used in cybersecurity for an ICT system security analysis.

Keywords
cybersecurity, deep learning, network analysis, supervised machine learning, unsupervised machine learning
DOI
10.1109/ITMS52826.2021.9615321
Hyperlink
https://ieeexplore.ieee.org/document/9615321

Roponena, E., Kampars, J., Gailītis, A., Strods, J. A Literature Review of Machine Learning Techniques for Cybersecurity in Data Centers. In: 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2021), Latvia, Riga, 14-15 October, 2021. Piscataway: IEEE, 2021, pp.1-6. ISBN 978-1-6654-0616-1. e-ISBN 978-1-6654-0615-4. Available from: doi:10.1109/ITMS52826.2021.9615321

Publication language
English (en)