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

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

doi:10.32628/CSEIT2410320

Authors

Shreyas T J U G Student, Department of Computer Science & Engineering, R. L. Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Ilyaz Pasha M Assistant Professor, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
Sindhu A M U G Student, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
T Hrushikesh U G Student, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author
U Kiran U G Student, Department of Computer Science Engineering, R L Jalappa Institute of Technology, Doddaballapur, Karnataka, India Author

DOI:

https://doi.org/10.32628/CSEIT2410320

Keywords:

Network Anomaly Detection, KNN, K-Prototype Algorithms

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.

Downloads

Download data is not yet available.

References

Mohiuddin A, Abdun NM, Jiankun H. Outlier detec- tion. In The State of the Art in Intrusion Prevention and Detection, Al-Sakib Khan Pathan (ed). Chapter: 1, Publisher: CRC Press: New York, USA, 2014. DOI: 10.1201/b16390-3 DOI: https://doi.org/10.1201/b16390-3

Bilge L, Balzarotti D, Robertson W, Kirda E, Kruegel C. Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis. Proceed- ings of the 28th Annual Computer Security Applica- tions Conference. 2012, 129–138 DOI: https://doi.org/10.1145/2420950.2420969

Münz G, Li S, Carle G. Traffic anomaly detection using k-means clustering. In Proceedings of Performance, Reliability and Dependability Evaluation of Communi- cation Networks and Distributed Systems, 4 GI / ITG Workshop MMBnet. Hamburg, Germany. 2007

Hofstede R, Bartos V, Sperotto A, Pras A. Towards real-time intrusion detection for NetFlow and IPFIX. In: 9th International Conference on Network and Ser- vice Management, CNSM 2013, October 2013, Zürich, Switzerland. 2013, 14–18 DOI: https://doi.org/10.1109/CNSM.2013.6727841

Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the Third SIAM International Conference on Data Mining. 2003 DOI: https://doi.org/10.1137/1.9781611972733.3

Gogoi P, Bhattacharyya DK, Borah B, Kalita JK. A survey of outlier detection methods in network anom- aly identification. The Computer Journal 2011; 54(4):570–588. DOI: https://doi.org/10.1093/comjnl/bxr026

Chandola V, Banerjee A, Kumar V. Anomaly detec- tion: a survey. ACM Computing Surveys (CSUR) 2009; 41(3):15–58. DOI: https://doi.org/10.1145/1541880.1541882

Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX. 2000, 93–104. DOI: https://doi.org/10.1145/335191.335388

Garšva E, Paulauskas N, Gražulevičius G, Gulbinovič L. Packet inter-arrival time distribution in academic computer network. Elektronika ir elektrotechnika. Elec- tronics and Electrical Engineering 2014; 20(3):87–90. DOI: https://doi.org/10.5755/j01.eee.20.3.6683