Detecting Anomalies in 5G Networks: A Machine Learning Approach for Robust Solutions

Shreyas T J, Ilyaz Pasha M, Sindhu A M, T Hrushikesh and U Kiran

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 3, 161-166

Abstract: The telecommunications industry is advancing rapidly with the introduction of 5G technology, which promises enhanced broadband cellular networks. However, alongside the benefits come challenges, particularly in ensuring the security of these networks against cyber attacks. This paper focuses on Network Anomaly Detection (NAD) in 5G, aiming to detect and prevent abnormal behaviors within the network that could signify potential security threats. Various methods, including machine learning algorithms, are explored to achieve effective NAD. Specifically, the KNN and K-prototype algorithms are tested alongside an integrated approach, with the integrated method demonstrating superior performance.

Keywords: Network Anomaly Detection; KNN; K-Prototype Algorithms (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410320
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i3:id:187

DOI: 10.32628/CSEIT2410320

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