Multi-domain anomaly detection in a 5G network

TL;DR

This paper introduces a multi-domain anomaly detection approach for 5G networks that analyzes traffic correlations across temporal, semantic, and topological dimensions to improve security insights.

Contribution

It presents a novel method that studies the correlations among multiple traffic domains for comprehensive anomaly detection in 5G networks.

Findings

Effective detection of anomalies across multiple traffic domains

Enhanced explainability of network anomalies

Improved security monitoring in dynamic 5G environments

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.