Machine Learning Isolation Forest-based Detection of Distributed Denial of Service Attacks on 5G Core Networks

Abstract

In the modern networking landscape, with the emergence of 5G and 6G networks, the ecosystem has become increasingly heterogeneous. This added complexity also increases the potential for more prominent cyber-threats, resulting in more devastating consequences for society. Many threat actors are being funded by governments, enabling them to perform more sophisticated attacks that existing perimeter security models are unable to tackle. Consequently, better solutions are required to detect and mitigate these unknown threats. In this thesis, we propose a novel anomaly detection method based on machine learning that can be utilized to defend 5G core networks. The proposed approach is designed to address situations where threat actors target the control plane and attempt to perform Distributed Denial of Service (DDoS) Attacks.

Keywords: 5G; Machine Learning; Transformer-based model; AI; Mobile Networking