Multi-domain anomaly detection in a 5G network

Abstract

With the advent of 5G, mobile networks are becoming more dynamic and will therefore present a wider attack surface. To secure these new systems, we propose a multi-domain anomaly detection method that is distinguished by the study of traffic correlation on three dimensions: temporal by analyzing message sequences, semantic by abstracting the parameters these messages contain, and topological by linking them in the form of a graph. Unlike traditional approaches, which are limited to considering these domains independently, our method studies their correlations to obtain a global, coherent and explainable view of anomalies.