5Greplay: a 5G Network Traffic Fuzzer - Application to Attack Injection

Abstract

The fifth generation of mobile broadband is more than just an evolution to provide more mobile bandwidth, massive machine-type communications, and ultra-reliable and low-latency communications. It relies on a complex, dynamic and heterogeneous environment that implies addressing numerous testing and security challenges. In this paper we present 5Greplay, an open-source 5G network traffic fuzzer that enables the evaluation of 5G components by replaying and modifying 5G network traffic by creating and injecting network scenarios into a target that can be a 5G core service (e.g., AMF, SMF) or a RAN network (e.g., gNodeB). The tool provides the ability to alter network packets online or offline in both control and data planes in a very flexible manner. The experimental evaluation conducted against open-source based 5G platforms, showed that the target services accept traffic being altered by the tool, and that it can reach up to 9.56 Gbps using only 1 processor core to replay 5G traffic.

A Network Traffic Mutation Based Ontology, and Its Application to 5G Networks

“…The work described in this paper extends the research and open-source solution 5Greplay, presented by the authors in [2], [3]. Notably, in this paper, we present an ontology based on mutation techniques for the modelling of cybersecurity attacks and its application to 5G networks, that we demonstrate by means of our 5G network traffic fuzzer, 5Greplay.…”

“…We embody our ontology in our network traffic fuzzer, 5Greplay 2 . It is an open-source solution that generates mutants of the network traffic by using mutant operators, in order to perform specified security and functional tests on a system, as well as, fuzzing testing.…”

“…It is an open-source solution that generates mutants of the network traffic by using mutant operators, in order to perform specified security and functional tests on a system, as well as, fuzzing testing. In [2] we presented a first version of the tool, its rule syntax, and original mutant operators, that we extend in this article. 5Greplay main workflow is depicted in Figure 6.…”

“…To illustrate our ontology, we have formalized the experiments we already performed in our previous work, where we presented our 5Greplay fuzzer [2].…”

Towards improving explainability, resilience and performance of cybersecurity analysis of 5G/IoT networks (work-in-progress paper)

“…In the on-going work presented here, we aim to tackle the above challenges by: (1) producing real-world datasets for AI training, especially for 5G and encrypted network traffic thanks to our open source 5Greplay tool [3]; (2) enabling explainability features of existing AI algorithms in our different AI-based systems, such as MMT-Probe for anomaly detection and MMT-RCA for Root Cause Analysis; and, (3) considering the security threats that emerge from the rapid adoption of AI algorithms in 5G/IoT networks. Existing challenges of AI in 5G/IoT networks motivate us to make the ML models more accurate, explainable and robust before they are integrated into complex systems.…”