Abstract
The amount of traffic carried over wireless networks is growing rapidly and is being driven by many factors. The telecommunications industry is undergoing a major transformation towards 5G networks in order to fulfill the needs of existing and emerging use cases. The paper studies the existing vulnerabilities of the 5G ecosystem. Considering this study, we propose a new cyber security model that considers machine learning algorithms. The function contains Firewall and IDS/IPS. We integrate the described model into an existing 5G architecture. The methodology and the pseudo code of the algorithmic core is provided. The paper also studies the efficiency of this approach. The tests are performed in a test laboratory, which includes a server and 60 raspberry pi hardware systems that are used in order to simulate attacks on the server. The tests show that the offered approach identifies DOS/DDOS attack much better than methods described in the related works. The paper also suggests the improvement strategy, which will be implemented in the future versions of the system.
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Acknowledgment
The work was financed by Shota Rustaveli National Science Foundation and Caucasus University in the frame of the [CARYS-19-121] grant and Caucasus University grant project.
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Iavich, M., Gnatyuk, S., Odarchenko, R., Bocu, R., Simonov, S. (2021). The Novel System of Attacks Detection in 5G. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_47
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DOI: https://doi.org/10.1007/978-3-030-75075-6_47
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