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

Author

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  • Shreyas T J
  • Ilyaz Pasha M
  • Sindhu A M
  • T Hrushikesh
  • U Kiran

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.

Suggested Citation

  • Shreyas T J & Ilyaz Pasha M & Sindhu A M & T Hrushikesh & U Kiran, 2024. "Detecting Anomalies in 5G Networks : A Machine Learning Approach for Robust Solutions," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 161-166, June.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:187
    DOI: 10.32628/CSEIT2410320
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410320
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