Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Imanbayev A. Tynymbayev S. Odarchenko R. Gnatyuk S. Berdibayev R. Baikenov A. Kaniyeva N.
December 2022 MDPI

Sensors
2022 #22 Issue 24

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

5G , cybersecurity , dataset , deep learning , intrusion detection system , machine learning

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Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050000, Kazakhstan
Information Security Laboratory, Almaty University of Power Engineering and Telecommunications, Almaty, 050013, Kazakhstan
Department of Telecommunication and Radioelectronic Systems, National Aviation University, Kyiv, 03058, Ukraine

Faculty of Information Technology
School of Information Technology and Engineering
Information Security Laboratory
Department of Telecommunication and Radioelectronic Systems

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