ADSeq-5GCN: Anomaly Detection from Network Traffic Sequences in 5G Core Network Control Plane
Zixu Tian, Rajendra Shivaji Patil, Mohan Gurusamy, Joshua McCloud · 2023
The service-based architecture (SBA) of 5G Core (5GC) introduces significant landscape changes to the modern communication and network system, and the network slicing enables different Network Functions (NFs) to meet diverse service requirements. However, with the broadening interface, some key NFs may become more vulnerable to internal hostile NFs or external malicious entities, which pose severe threats to the control-plane components in the 5GC network (5GCN). In this paper, we propose ADSeq-5GCN, a network-level anomaly detection framework based on modeling network traffic sequences. Our framework focuses on the control plane of 5GCN, where the network traffic is captured and analyzed for anomalies. We use a sequence model, Bidirectional Long Short Terms Memory (Bi-LSTM) networks, to learn normal NF-to-NF interactions and detect anomalies based on incorrect service event prediction. We evaluate our proposed framework on a 5GCN testbed with Free5GC and UERANSIM under various scenarios. Our results demonstrate the overwhelming performance of our proposed framework over the baseline models.