Abstract
Fifth generation mobile networks (5G) leverage the power of edge computing to move vital services closer to end users. With critical 5G core network components located at the edge there is a need for detecting malicious signalling traffic to mitigate potential signalling attacks between the distributed Network Functions (NFs). A prerequisite for detecting anomalous signalling is a network traffic dataset for the identification and classification of normal traffic profiles. To this end, we utilise a 5G Core Network (5GC) simulator to execute test scenarios for different 5G procedures and use the captured network traffic to generate a dataset of normalised service interactions in the form of packet captures. We then apply machine learning techniques (supervised learning) and do a comparative analysis on accuracy, which uses three features from the traffic meta-data. Our results show that the identification of 5G service use by applying ML techniques offer a viable solution to classifying normal services from network traffic metadata alone. This has potential advantages in forecasting service demand for resource allocation in the dynamic 5GC environment and provide a baseline for performing anomaly detection of NF communication for detecting malicious traffic within the 5G Service Based Architecture (SBA).